18) it is possible to distribute the predicted values of the output vector along a different scan order (e.g., one scan order: one other scan order: Other strategies may be carried out. In other examples, the mode index ‘mode’ is not necessarily in the range 0 to 35 (other ranges may be defined). Further, it is not necessary that each of the three sets S0, S1, S2 has 18 matrices (hence, instead of expressions like mode ≥ 18, it is possible to mode ≥ n0, n1, n2, which are the number of matrixes for each set of matrixes S0, S1, S2, respectively). Further, the sets may have different numbers of matrixes each (for example, it may be that So has 16 matrixes S1 has eight matrixes, and S2 has six matrixes). The mode and transposed information are not necessarily stored and/or transmitted as one combined mode index ‘mode’: in some examples there is the possibility of signalling explicitly as a transposed flag and the matrix index (0-15 for S0, 0-7 for S1 and 0-5 for S2). In some cases, the combination of the transposed flag and matrix index may be interpreted as a set index. For example, there may be one bit operating as transposed flag, and some bits indicating the matrix index, collectively indicated as “set index". 6.3 Generation of the reduced prediction Signal by matrix vector multiplication Here, features are provided regarding step 812. Out of the reduced input vector bdryred (boundary vector 17P) one may generate a reduced prediction signal predred . The latter signal may be a signal on the downsampled block of width Wred and height Hred. Here, Wred and Hred may be defined as: Wred = 4, Hred = 4: if max(W,H) ≤ 8, Wred = min(W, 8) , Hred = min (H, 8) ; else. The reduced prediction signal predred may be computed by calculating a matrix vector-product and adding an offset: predred = A · bdryred + b. Here, A is a matrix (e.g., prediction matrix 17M) that may have Wred * Hred rows and 4 columns if W=H=4 and 8 columns in all other cases and b is a vector that may be of size Wred * Hred. If W = H = 4, then A may have 4 columns and 16 rows and thus 4 multiplications per sample may be needed in that case to compute predred. In all other cases, A may have 8 columns and one may verify that in these cases one has 8 * Wred * Hred ≤ 4 * W * H, i.e. also in these cases, at most 4 multiplications per sample are needed to compute predred. The matrix A and the vector b may be taken from one of the sets S0, S1, S2 as follows. One defines an index idx = idx(W, H) by setting idx(W, H) = 0, if W = H = 4, idx(W, H) = 1, if max(W, H) = 8 and idx(W, H) = 2 in all other cases. Moreover, one may put m = mode, if mode < 18 and m = mode — 17, else. Then, if idx ≤ 1 or idx = 2 and min(W, H) > 4, one may put and In the case that idx= 2 and min(W,H) = 4, one lets A be the matrix that arises by leaving out every row of that, in the case W=4, corresponds to an odd x- coordinate in the downsampled block, or, in the case H=4, corresponds to an odd y-coordinate in the downsampled block. If mode ≥ 18, one replaces the reduced prediction signal by its transposed signal. In alternative examples, different strategies may be carried out. For example, instead of reducing the size of a larger matrix (“leave out”), a smaller matrix of S1 (idx=1) with Wred=4 and Hred=4 is used. I.e., such blocks are now assigned to S1 instead of S2. Other strategies may be carried out. In other examples, the mode index ‘mode’ is not necessarily in the range 0 to 35 (other ranges may be defined). Further, it is not necessary that each of the three sets S0, S1, S2 has 18 matrices (hence, instead of expressions like mode < 18, it is possible to mode < n0, n1, n2. which are the number of matrixes for each set of matrixes S0, S1, S2, respectively). Further, the sets may have different numbers of matrixes each (for example, it may be that S0 has 16 matrixes S1 has eight matrixes, and S2 has six matrixes). 6.4 Linear interpolation to generate the final prediction signal Here, features are provided regarding step 812. Interpolation of the subsampled prediction signal, on large blocks a second version of the averaged boundary may be needed. Namely, if min(W, H) > 8 and W ≥ H, one writes W= 8 * 2 and for 0 ≤ i < 8 defines If min(W, H) > 8 and H > W, one defines analogously. In addition or alternative, it is possible to have a “hard downsampling", in which the is equal to Also, can be defined analogously. At the sample positions that were left out in the generation of predred, the final prediction signal may arise by linear interpolation from predred (e.g., step 813 in examples of Figs. 7.2-7.4). This linear interpolation may be unnecessary, in some examples if W = H = 4 (e.g., example of Fig. 7.1). The linear interpolation may be given as follows (other examples are notwithstanding possible). It is assumed that W ≥ H. Then, if H ≥ Hred, a vertical upsampling of predred may be performed. In that case, predred may be extended by one line to the top as follows. If W = 8, predred may have width Wred = 4 and may be extended to the top by the averaged boundary signal e.g. as defined above. If W > 8, predred is of width Wred = 8 and it is extended to the top by the averaged boundary signal e.g. as defined above. One may write predred [x][-1] for the first line of predred. Then the signal on a block of width Wred and height 2 * Hred may be given as where 0 ≤ x < Wred and 0 ≤ y < Hred. The latter process may be carried out k times until 2k*Hred=H. Thus, if H= 8 or H= 16, it may be carried out at most once. If H= 32, it may be carried out twice. If H= 64, it may be carried out three times. Next, a horizontal upsampling operation may be applied to the result of the vertical upsampling. The latter upsampling operation may use the full boundary left of the prediction signal. Finally, if H > W, one may proceed analogously by first upsampling in the horizontal direction (if required) and then in the vertical direction. This is an example of an interpolation using reduced boundary samples for the first interpolation (horizontally or vertically) and original boundary samples for the second interpolation (vertically or horizontally). Depending on the block size, only the second or no interpolation is required. If both horizontal and vertical interpolation is required, the order depends on the width and height of the block. However, different techniques may be implemented: for example, original boundary samples may be used for both the first and the second interpolation and the order may be fixed, e.g. first horizontal then vertical (in other cases, first vertical then horizontal). Hence, the interpolation order (horizontal/vertical) and the use of reduced/original boundary samples may be varied. 6.5 Illustration of an example of the entire ALWIP process The entire process of averaging, matrix-vector-multiplication and linear interpolation is illustrated for different shapes in Figs. 7.1-7.4. Note, that the remaining shapes are treated as in one of the depicted cases. 1. Given a 4 x 4 block, ALWIP may take two averages along each axis of the boundary by using the technique of Fig. 7.1. The resulting four input samples enter the matrix-vector- multiplication. The matrices are taken from the set S0. After adding an offset, this may yield the 16 final prediction samples. Linear interpolation is not necessary for generating the prediction signal. Thus, a total of (4 * 16)/(4 * 4) = 4 multiplications per sample are performed. See, for example, Figs. 7.1. 2. Given an 8 x 8 block, ALWIP may take four averages along each axis of the boundary. The resulting eight input samples enter the matrix-vector-multiplication, by using the technique of Fig. 7.2. The matrices are taken from the set S1. This yields 16 samples on the odd positions of the prediction block. Thus, a total of (8 * 16)/(8 * 8) = 2 multiplications per sample are performed. After adding an offset, these samples may be interpolated, e.g., vertically by using the top boundary and, e.g., horizontally by using the left boundary. See, for example, Fig. 7.2. 3. Given an 8 x 4 block, ALWIP may take four averages along the horizontal axis of the boundary and the four original boundary values on the left boundary by using the technique of Fig. 7.3. The resulting eight input samples enter the matrix-vector- multiplication. The matrices are taken from the set S1. This yields 16 samples on the odd horizontal and each vertical positions of the prediction block. Thus, a total of (8 * 16)/(8 * 4) = 4 multiplications per sample are performed. After adding an offset, these samples are interpolated horizontally by using the left boundary, for example. See, for example, Figs. 7.3. The transposed case is treated accordingly. 4. Given a 16 x 16 block, ALWIP may take four averages along each axis of the boundary. The resulting eight input samples enter the matrix-vector-multiplication by using the technique of Fig. 7.2. The matrices are taken from the set S2. This yields 64 samples on the odd positions of the prediction block. Thus, a total of (8 * 64)/(16 * 16) = 2 multiplications per sample are performed. After adding an offset, these samples are interpolated vertically by using the top boundary and horizontally by using the left boundary, for example. See, for example, Figs. 7.2. See, for example, Figs. 7.4. For larger shapes, the procedure may be essentially the same and it is easy to check that the number of multiplications per sample is less than two. For Wx8 blocks, only horizontal interpolation is necessary as the samples are given at the odd horizontal and each vertical positions. Thus, at most (8 * 64)/(16 * 8) = 4 multiplications per sample are performed in these cases. Finally for Wx4 blocks with W>8, let 4kbe the matrix that arises by leaving out every row that correspond to an odd entry along the horizontal axis of the downsampled block. Thus, the output size may be 32 and again, only horizontal interpolation remains to be performed. At most (8 * 32)/(16 * 4) = 4 multiplications per sample may be performed. The transposed cases may be treated accordingly. 6.6 Number of parameters needed and complexity assessment The parameters needed for all possible proposed intra prediction modes may be comprised by the matrices and offset vectors belonging to the sets S0, S1, S2. All matrix-coefficients and offset vectors may be stored as 10-bit values. Thus, according to the above description, a total number of 14400 parameters, each in 10-bit precision, may be needed for the proposed method. This corresponds to 0,018 Megabyte of memory. It is pointed out that currently, a CTU of size 128 x 128 in the standard 4:2:0 chroma-subsampling consists of 24576 values, each in 10 bit. Thus, the memory requirement of the proposed intra-prediction tool does not exceed the memory requirement of the current picture referencing tool that was adopted at the last meeting. Also, it is pointed out that the conventional intra prediction modes require four multiplications per sample due to the PDPC tool or the 4-tap interpolation filters for the angular prediction modes with fractional angle positions. Thus, in terms of operational complexity the proposed method does not exceed the conventional intra prediction modes. 6.7 Signalization of the proposed intra prediction modes For luma blocks, 35 ALWIP modes are proposed, for example, (other numbers of modes may be used). For each Coding Unit (CU) in intra mode, a flag indicating if an ALWIP mode is to be applied on the corresponding Prediction Unit (PU) or not is sent in the bitstream. The signalization of the latter index may be harmonized with MRL in the same way as for the first CE test. If an ALWIP mode is to be applied, the index predmode of the ALWIP mode may be signaled using an MPM-list with 3 MPMS. Here, the derivation of the MPMs may be performed using the intra-modes of the above and the left PU as follows. There may be tables, e.g. three fixed tables map_angular_to_alwipidx, idx ∈ {0,1,2} that may assign to each conventional intra prediction mode predmodeAngular an ALWIP mode For each PU of width W and height H one defines and index idx(PU) = idx(W, H) ∈ {0,1,2} that indicates from which of the three sets the ALWIP-parameters are to be taken as in section 4 above. If the above Prediction Unit PUabove is available, belongs to the same CTU as the current PU and is in intra mode, if idx(PU) = idx(PUabove ) and if ALWIP is applied on PUabove with ALWIP-mode one puts If the above PU is available, belongs to the same CTU as the current PU and is in intra mode and if a conventional intra prediction mode is applied on the above PU, one puts In all other cases, one puts which means that this mode is unavailable. In the same way but without the restriction that the left PU needs to belong to the same CTU as the current PU, one derives a mode Finally, three fixed default lists listidx, idx ∈ {0,1,2} are provided, each of which contains three distinct ALWIP modes. Out of the default list listidx(PU) and the modes and one constructs three distinct MPMs by substituting -1 by default values as well as eliminating repetitions. The herein described embodiments are not limited by the above described Signalization of the proposed intra prediction modes. According to art alternative embodiment, no MPMs and/or mapping tables are used for MIP (ALWIP). 6.8 Adapted MPM-list derivation for conventional luma and chroma intra-prediction modes The proposed ALWIP-modes may be harmonized with the MPM-based coding of the conventional intra-prediction modes as follows. The luma and chroma MPM-list derivation processes for the conventional intra-prediction modes may use fixed tables map_lwip_to_angularidx, idx ∈ {0,1,2}, mapping an ALWIP-mode predmodeLWIP on a given PU to one of the conventional intra-prediction modes predmodeAngular = map_lwip_to__angularidx(PU)[predmodeLWIP]. For the luma MPM-list derivation, whenever a neighboring luma block is encountered which uses an ALWIP-mode predmodeLWIP, this block may be treated as if it was using the conventional intra-prediction mode predmodeAnguiar. For the chroma MPM-list derivation, whenever the current luma block uses an LWIP-mode, the same mapping may be used to translate the ALWIP-mode to a conventional intra prediction mode. It is clear, that the ALWIP-modes can be harmonized with the conventional intra-prediction modes also without the usage of MPMs and/or mapping tables. It is, for example, possible that for the chroma block, whenever the current luma block uses an ALWIP-mode, the ALWIP-mode is mapped to a planar-intra prediction mode. 7. Implementation efficient embodiments Let’s briefly summarize the above examples as they might form a basis for further extending the embodiments described herein below. For predicting a predetermined block 18 of the picture 10, using a plurality of neighbouring samples 17a, c is used. A reduction 100, by averaging, of the plurality of neighbouring samples has been done to obtain a reduced set 102 of samples values lower, in number of samples, than compared to the plurality of neighbouring samples. This reduction is optional in the embodiments herein and yields the so called sample value vector mentioned in the following. The reduced set of sample values is the subject to a linear or affine linear transformation 19 to obtain predicted values for predetermined samples 104 of the predetermined block. It is this transformation, later on indicated using matrix A and offset vector b which has been obtained by machine learning (ML) and should be implementation efficiently preformed. 2By interpolation, prediction values for further samples 108 of the predetermined block are derived on the basis of the predicted values for the predetermined samples and the plurality of neighbouring samples. It should be said that, theoretically, the outcome of the affine/linear transformation could be associated with non-full-pel sample positions of block 18 so that all samples of block 18 might be obtained by interpolation in accordance with an alternative embodiment. No interpolation might be necessary at all, too. The plurality of neighbouring samples might extend one-dimensionally along two sides of the predetermined block, the predetermined samples are arranged in rows and columns and, along at least one of the rows and columns, wherein the predetermined samples may be positioned at every nth position from a sample (112) of the predetermined sample adjoining the two sides of the predetermined block. Based on the plurality of neighbouring samples, for each of the at least one of the rows and the columns, a support value for one (118) of the plurality of neighbouring positions might be determined, which is aligned to the respective one of the at least one of the rows and the columns, and by interpolation, the prediction values for the further samples 108 of the predetermined block might be derived on the basis of the predicted values for the predetermined samples and the support values for the neighbouring samples aligned to the at least one of rows and columns. The predetermined samples may be positioned at every nth position from the sample 112 of the predetermined sample which adjoins the two sides of the predetermined block along the rows and the predetermined samples may be positioned at every mth position from the sample 112 of the predetermined sample which adjoins the two sides of the predetermined block along the columns, wherein n,m>1. It might be that n=m. Along at least one of the rows and column, the determination of the support values may be done by averaging (122), for each support value, a group 120 of neighbouring samples within the plurality of neighbouring samples which includes the neighbouring sample 118 for which the respective support value is determined. The plurality of neighbouring samples may extend one-dimensionally along two sides of the predetermined block and the reduction may be done by grouping the plurality of neighbouring samples into groups 110 of one or more consecutive neighbouring samples and performing an averaging on each of the group of one or more neighbouring samples which has more than two neighbouring samples. For the predetermined block, a prediction residual might be transmitted in the data stream. It might be derived therefrom at the decoder arid the predetermined block be reconstructed using the prediction residual and the predicted values for the predetermined samples. At the encoder, the prediction residual is encoded into the data stream at the encoder. The picture might be subdivided info a plurality of blocks of different block sizes, which plurality comprises the predetermined block., Then, it might be that the linear or affine linear transformation for block 18 is selected depending on a width W and height H of the predetermined block such that the linear or affine linear transformation selected for the predetermined block is selected out of a first set of linear or affine linear transformations as long as the width W and height H of the predetermined block are within a first set of width/height pairs and a second set of linear or affine linear transformations as long as the width W and height H of the predetermined block are within a second set of width/height pairs which is disjoint to the first set of width/height pairs. Again, later on it gets clear that the affine/linear transformations are represented by way of other parameters, namely weights of C and, optionally, offset and scale parameters. Decoder and encoder may be configured to subdivide the picture into a plurality of blocks of different block sizes, which comprises the predetermined block, and to select the linear or affine linear transformation depending on a width Wand height H of the predetermined block such that the linear or affine linear transformation selected for the predetermined block is selected out of a first set of linear or affine linear transformations as long as the width W and height H of the predetermined block are within a first set of width/height pairs, a second set of linear or affine linear transformations as long as the width W and height H of the predetermined block are within a second set of width/height pairs which is disjoint to the first set of width/height pairs, and a third set of linear or affine linear transformations as long as the width W and height H of the predetermined block are within a third set of one or more width/height pairs, which is disjoint to the first and second sets of width/height pairs. The third set of one or more width/height pairs merely comprises one width/height pair, W', H’, and each linear or affine linear transformation within first set of linear or affine linear transformations is for transforming N’ sample values to W’*H’ predicted values for an W’xH' array of sample positions. Each of the first and second sets of width/height pairs may comprise a first width/height pairs Wp,Hp with Wp being unequal to Hp and a second width/height pair Wq,Hq with Hq=Wp and Wq=Hp. Each of the first and second sets of width/height pairs may additionally comprise a third width/height pairs Wp,Hp with Wp being equal to Hp and Hp > Hq. For the predetermined block, a set index might be transmitted in the data stream, which indicates which linear or affine linear transformation to be selected for block 18 out of a predetermined set of linear or affine linear transformations. The plurality of neighbouring samples may extend one-dimensionally along two sides of the predetermined block and the reduction may be done by, for a first subset of the plurality of neighbouring samples, which adjoin a first side of the predetermined block, grouping the first subset into first groups 110 of one or more consecutive neighbouring samples and, for a second subset of the plurality of neighbouring samples, which adjoin a second side of the predetermined block, grouping the second subset into second groups 110 of one or more consecutive neighbouring samples and performing an averaging on each of the first and second groups of one or more neighbouring samples which has more than two neighbouring samples, so as to obtain first sample values from the first groups and second sample values for the second groups. Then, the linear or affine linear transformation may be selected depending on the set index out of a predetermined set of linear or affine linear transformations such that two different states of the set index result into a selection of one of the linear or affine linear transformations of the predetermined set of linear or affine linear transformations, the reduced set of sample values may be subject to the predetermined linear or affine linear transformation in case of the set index assuming a first of the two different states in form of a first vector to yield an output vector of predicted Valdes, and distribute the predicted values of the output vector along a first scan order onto the predetermined samples of the predetermined block and in case of the set index assuming a second of the two different states in form of a second vector, the first and second vectors differing so that components populated by one of the first sample values in the first vector are populated by one of the second sample values in the second vector, and components populated by one of the second sample values in the first vector are populated by one of the first sample values in the second vector, so as to yield an output vector of predicted values, and distribute the predicted values of the output vector along a second scan order onto the predetermined samples of the predetermined block which is transposed relative to the first scan order. Each linear or affine linear transformation within first set of linear or affine linear transformations may be for transforming N1 sample values to w1*h1 predicted values for an w1xh1 array of sample positions and each linear or affine linear transformation within second set of linear or affine linear transformations is for transforming N2 sample values to w2*h2 predicted values for an w2xh2 array of sample positions, wherein for a first predetermined one of the first set of width/height pairs, w1 may exceed the width of the first predetermined width/height pair or hi may exceed the height of the first predetermined width/height pair, and for a second predetermined one of the first set of width/height pairs neither w1 may exceed the width of the second predetermined width/height pair nor h1 exceeds the height of the second predetermined width/height pair. The reducing (100), by averaging, the plurality of neighbouring samples to obtain the reduced set (102) of samples values might then be done so that the reduced set 102 of samples values has N1 sample values if the predetermined block is of the first predetermined width/height pair and if the predetermined block is of the second predetermined width/height pair, and the subjecting the reduced set of sample values to the selected linear or affine linear transformation might be performed by using only a first sub-portion of the selected linear or affine linear transformation which is related to a subsampling of the w1xh1 array of sample positions along width dimension if w1 exceeds the width of the one width/height pair, or along height dimension if h1 exceeds the height of the one width/height pair if the predetermined block is of the first predetermined width/height pair, and the selected linear or affine linear transformation completely if the predetermined block is of the second predetermined width/height pair. Each linear or affine linear transformation within first set of linear or affine linear transformations may be for transforming N1 sample values to w1*h1 predicted values for an w1xh1 array of sample positions with w1=h1 and each linear or affine linear transformation within second set of linear or affine linear transformations is for transforming N2 sample values to w2*h2 predicted values for an w2xh2 array of sample positions with w2=h2. All of the above described embodiments are merely illustrative in that they may form the basis for the embodiment described herein below. That is, above concepts and details shall serve to understand the following embodiments and shall serve as a reservoir of possible extensions and amendments of the embodiments described herein below. In particular, many of the above described details are optional such as the averaging of neighboring samples, the fact the neighboring samples are used as reference samples and so forth. More generally, the embodiments described herein assume that a prediction signal on a rectangular block is generated out of already reconstructed samples such as an intra prediction signal on a rectangular block is generated out of neighboring, already reconstructed samples left and above the block. The generation of the prediction signal is based on the following steps. 1. Out of the reference samples, called boundary sample now without, however, excluding the possibility of transferring the description to reference samples positioned elsewhere, samples may be extracted by averaging. Here, the averaging is carried out either for both the boundary samples left and above the block or only for the boundary samples on one of the two sides. If no averaging is carried out on a side, the samples on that side are kept unchanged. 2. A matrix vector multiplication, optionally followed by addition of an offset, is carried out where the input vector of the matrix vector multiplication is either the concatenation of the averaged boundary samples left of the block and the original boundary samples above the block if averaging was applied only on the left side, or the concatenation of the original boundary samples left of the block and the averaged boundary samples above the block if averaging was applied only on the above side or the concatenation of the averaged boundary samples left of the block and the averaged boundary samples above the block if averaging was applied on both sides of the block. Again, alternatives would exist, such as ones where averaging isn’t used at all. 3. The result of the matrix vector multiplication and the optional offset addition may optionally be a reduced prediction signal on a subsampled set of samples in the original block. The prediction signal at the remaining positions may be generated from the prediction signal on the subsampled set by linear interpolation. The computation of the matrix vector product in Step 2 should preferably be carried out in integer arithmetic. Thus, if x = (x1, ...,xn) denotes the input for the matrix vector product, i.e. x denotes the concatenation of the (averaged) boundary samples left and above the block, then out of x, the (reduced) prediction signal computed in Step 2 has should be computed using only bit shifts, the addition of offset vectors, and multiplications with integers. Ideally, the prediction signal in Step 2 would be given as Ax + b where b is an offset vector that might be zero and where A is derived by some machine-learning based training algorithm. However, such a training algorithm usually only results in· a matrix A = Afloat that is given in floating point precision. Thus, one is faced with the problem to specify integer operations in the aforementioned sense such that the expression Afloatx is well approximated using these integer operations. Here, it is important to mention that these integer operations are not necessarily chosen such that they approximate the expression Afloatx assuming a uniform distribution of the vector x but typically take into account that the input vectors x for which the expression Afloatx is to be approximated are (averaged) boundary samples from natural video signals where one can expect some correlations between the components xi of x. Fig. 8 shows an improved ALWIP-prediction. Samples of a predetermined block can be predicted based on a first matrix-vector product between a matrix A 1100 derived by some machine-learning based training algorithm and a sample value vector 400. Optionally an offset b 1110 can be added. To achieve an integer approximation or a fixed-point approximation of this first matrix-vector product, the sample value vector can undergo an invertible linear transformation 403 to determine a further vector 402. A second matrix-vector product between a further matrix B 1200 and the further vector 402 can equal the result of the first matrix-vector product. Because of the features of the further vector 402 the second matrix-vector product can be integer approximated by a matrix-vector product 404 between a predetermined prediction matrix C 405 and the further vector 402 plus a further offset 408. The further vector 402 and the further offset 408 can consist of integer or fixed-point values. All components of the further offset are, for example, the same. The predetermined prediction matrix 405 can be a quantized matrix or a matrix to be quantized. The result of the matrix-vector product 404 between the predetermined prediction matrix 405 and the further vector 402 can be understood as a prediction vector 406. In the following more details regarding this integer approximation are provided. Possible Solution according to an example I: Subtracting and adding mean values One possible incorporation of an integer approximation of an expression Afloatx useable in a scenario above is to replace the i0-th component i.e. a predetermined component 1500, of x, i.e. the sample value vector 400, by the mean value mean (x), i.e. a predetermined value 1400, of the components of x and to subtract this mean value from all other components. In other words, the invertible linear transform 403, as shown in Fig. 9a, is defined such that a predetermined component 1500 of the further vector 402 becomes a, and each of other components of the further vector 402, except the predetermined component 1500, equal a corresponding component of the sample value vector minus a, wherein a is a predetermined value 1400 which is, for example, an average, such as an arithmetic mean or weighted average, of components of the sample value vector 400.This operation on the input is given by an invertible transform T 403 that has an obvious integer implementation in particular if the dimension n of x is a power of two. Since Afloat = (AfloatT-1)T, if one does such a transformation on the input x, one has to find an integral approximation of the matrix vector product By, where B = (AfloatT -1) and y = Tx. Since the matrix-vector product Afloatx represents a prediction on a rectangular block, i.e. a predetermined block, and since x is comprised by (e.g., averaged) boundary samples of that block, one should expect that in the case where all sample values of x are equal, i.e. where xi = mean(x) for all i, each sample value in the prediction signal Afloatx should be close to mean(x ) or be exactly equal to mean(x). This means that one should expect that the i0-th column, i.e. the column corresponding to the predetermined component, of B is very close or equal to a column that consist only of ones. Thus, if M(i0), i.e. an integer matrix 1300, is the matrix whose i0th column consists of ones and all of whose other columns are zero, writing By = Cy + M(i0)y with C = B — M(i0), one should expect that the i0-th column of C, i.e. the predetermined prediction matrix 405, has rather small entries or is zero, as shown in Fig. 9b. Moreover, since the components of x are correlated, one can expect that for each the i-th component yi = xi - mean(x) of y often has a much smaller absolute value than the i-th component of x. Since the matrix M(i0) is an integer matrix, an integer approximation of By is achieved if an integer approximation of Cy is given and, by the above arguments, one can expect that the quantization error that arises by quantizing each entry of C in a suitable way should only marginally impact the error in the resulting quantization of By resp. of Afloatx. The predetermined value 1400 is not necessarily the mean value mean (x). The herein described integer approximation of the expression Afloatx can also be achieved with the following alternative definitions of the predetermined value 1400: In another possible incorporation of an integer approximation of an expression Afloatx, the i0-th component xio of x remains unaltered and the same value xio is subtracted from all other components. That is, and for each In other words, the predetermined value 1400 can be a component of the sample value vector 400 corresponding to the predetermined component 1500. Alternatively, the predetermined value 1400 is a default value or a value signaled in a data stream into which a picture is coded. The predetermined value 1400 equals, for example, 2bitdepth-1. In this case, the further vector 402 can be defined by y0=2bitdepth-1 and yi=xi-x0 for i>0. Alternatively, the predetermined component 1500 becomes a constant minus the predetermined value 1400. The constant equals, for example, 2bitdepth-1. According to an embodiment, the predetermined component 1500 of the further vector y 402 equals 2bitdepth-1 minus a component of the sample value vector 400 corresponding to the predetermined component 1500 and all other components of the further vector 402 equal the corresponding component of the sample value vector 400 minus the component of the sample value vector 400 corresponding to the predetermined component 1500. It is, for example, advantageous if the predetermined value 1400 has a small deviation from prediction values of samples of the predetermined block. According to an embodiment, the apparatus 1000 is configured to comprise a plurality of invertible linear transforms 403, each of which is associated with one component of the further vector 402. Furthermore, the apparatus is, for example, configured to select the predetermined component 1500 out of the components of the sample value vector 400 and use the invertible linear transform 403 out of the plurality of invertible linear transforms which is associated with the predetermined component 1500 as the predetermined invertible linear transform. This is, for example, due to different positions of the i0th row, i.e. a row of the invertible linear transform 403 corresponding to the predetermined component, dependent on a position of the predetermined component in the further vector. If, for example, the first component, i.e. y1, of the further vector 402 is the predetermined component, the i0th row would replace the first row of the invertible linear transform. As shown in Fig. 9b, matrix components 414 of the predetermined prediction matrix C 405 within a column 412, i.e. an i0th column, of the predetermined prediction matrix 405 which corresponds to the predetermined component 1500 of the further vector 402 are, for example, all zero. In this case, the apparatus is, for example, configured to compute the matrix-vector product 404 by performing multiplications by computing a matrix vector product 407 between a reduced prediction matrix C’ 405 resulting from the predetermined prediction matrix C 405 by leaving away the column 412 and an even further vector 410 resulting from the further vector 402 by leaving away the predetermined component 1500, as shown in Fig. 9c. Thus a prediction vector 406 can be calculated with less multiplications. As shown in Figs. 8, 9b and 9c, the apparatus 1000 can be configured to, in predicting the samples of the predetermined block on the basis of the prediction vector 406, compute for each component of the prediction vector 406 a sum of the respective component and a, i.e. the predetermined value 1400. This summation can be represented by a sum of the prediction vector 406 and a vector 409 with all components of the vector 409 being equal to the predetermined value 1400, as shown in Fig. 8 and Fig. 9c. Alternatively the summation can be represented by a sum of the prediction vector 406 and a matrix-vector product 1310 between an integer matrix M 1300 and the further vector 402, as shown in Fig. 9b, wherein matrix components of the integer matrix 1300 are 1 within a column, i.e. an i0th column, of the integer matrix 1300 which corresponds to the predetermined component 1500 of the further vector 402, and all other components are, for example, zero. A result of a summation of the predetermined prediction matrix 405 and the integer matrix 1300 equals or approximates, for example, the further matrix 1200, shown in Fig. 8. In other words, a matrix, i.e. the further matrix B 1200, which results from summing each matrix component of the predetermined prediction matrix C 405 within a column 412, i.e. the i0th column, of the predetermined prediction matrix 405, which corresponds to the predetermined component 1500 of the further vector 402, with one, (i.e. matric B) times the invertible linear transform 403 corresponds, for example, to a quantized version of a machine learning prediction matrix A 1100, as shown in Fig. 8, Fig. 9a and Fig. 9b. The summing of each matrix component of the predetermined prediction matrix C 405 within the i0th column 412 with one can correspond to the summation of the predetermined prediction matrix 405 and the integer matrix 1300, as shown in Fig. 9b. As shown in Fig. 8 the machine learning prediction matrix A 1100 can equal the result of the further matrix 1200 times the invertible linear transform 403. This is due to A · x = BT · yT-1. The predetermined prediction matrix 405 is, for example, a quantized matrix, an integer matrix and/or a fixed-point matrix, whereby the quantized version of the machine learning prediction matrix A 1100 can be realized. Matrix multiplication using integer operations only For a low complexity implementation (in terms of complexity of adding and multiplying scalar values, as well as in terms of storage required for the entries of the partaking matrix), it is desirable to perform the matrix multiplications 404 using integer arithmetic only. To calculate an approximation of z = Cy, i.e. using operations on integers only, the real values Ci,j must be mapped to integer values according to an embodiment. This can be done for example by uniform scalar quantization, or by taking into account specific correlations between values yi. The integer values represent, for example fixed-point numbers that can each be stored with a fixed number of bits n_bits, for example n_bits=8. The matrix vector product 404 with a matrix, i.e. the predetermined prediction matrix 405, of size m x n can then be carried out like shown in this pseudo code, where <<, >> are arithmetic binary left- and right-shift operations and +, - and * operate on integer values only. (1) final_offset = 1 << (right_shift_result - 1); for i in 0...m-1 { accumulator = 0 for j in 0...P-1 { accumulator: = accumulator + y[j]*C[i,j] } z[i] = (accumulator + final_offset) >> right_shift_result; } Here, the array C, i.e. the predetermined prediction matrix 405, stores the fixed point numbers, for example, as integers. The final addition of final_offset and the right-shift operation with right_shift_result reduce precision by rounding to obtain a fixed point format required at the output. To allow for an increased range of real values representable by the integers in C, two additional matrices offseti,j and scalei,j can be used, as shown in the embodiments of Fig. 10 and Fig. 11, such that each coefficient of yj in the matrix-vector product is given by The values offseti,j and scalei,j are themselves integer values. For example these integers can represent fixed-point numbers that can each be stored with a fixed number of bits, for example 8 bits, or for example the same number of bits n_bits that is used to store the values In other words, the apparatus 1000 is configured to represent the predetermined prediction matrix 405 using prediction parameters, e.g. integer values and the values offseti,j and scalei,j, and to compute the matrix-vector product 404 by performing multiplications and summations on the components of the further vector 402 and the prediction parameters and intermediate results resulting therefrom, wherein absolute values of the prediction parameters are representable by an n-bit fixed point number representation with n being equal to or lower than 14, or, alternatively, 10, or, alternatively, 8. For instance, the components of the further vector 402 are multiplied with the prediction parameters to yield products as intermediate results which, in turn, are subject to, or form addends of, a summation. According to an embodiment, the prediction parameters comprise weights each of which is associated with a corresponding matrix component of the prediction matrix. In other words, the predetermined prediction matrix is, for example, replaced or represented by the prediction parameters. The weights are, for example, integer and/or fixed point values. According to an embodiment, the prediction parameters further comprise one or more scaling factors, e.g. the values scalei,j, each of which is associated with one or more corresponding matrix components of the predetermined prediction matrix 405 for scaling the weight, e.g. an integer value associated with the one or more corresponding matrix component of the predetermined prediction matrix 405. Additionally or Alternatively, the prediction parameters comprise one or more offsets, e.g. the values offseti,j, each of which is associated with one or more corresponding matrix components of the predetermined prediction matrix 405 for offsetting the weight, e.g. an integer value associated with the one or more corresponding matrix component of the predetermined prediction matrix 405. Claims:--- 1. Apparatus (3000) for decoding a predetermined block (18) of a picture (10) using intra-prediction, configured to derive a set-selective syntax element (522) from the data stream (12) which indicates whether the predetermined block (18) is to be predicted using one of a first set (508) of intra-prediction modes comprising a DC intra prediction mode (506) and angular prediction modes (500), if the set-selective syntax element (522) indicates that the predetermined block (18) is to be predicted using one of the first set (508) of intra-prediction modes, form a list (528) of most probable intra-prediction modes on the basis of intra- prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, derive a MPM list index (534) from the data stream (12) which points into the list (528) of most probable intra-prediction modes onto a predetermined intra-prediction mode (3100), intra-predicting the predetermined block (18) using the predetermined intra-prediction mode (3100), if the set-selective syntax element (522) indicates that the predetermined block (18) is not to be predicted using one of the first set (508) of intra-prediction modes, derive a further index (540; 546) from the data stream (12) which indicates a predetermined matrix-based intra-prediction mode (3200) out of a second set (520) of matrix-based intra-prediction modes (510) by compute a matrix-vector product (512) between a vector (514, 400, 402) derived from reference samples (17) in a neighbourhood of the predetermined block (18) and a predetermined prediction matrix (516) associated with the predetermined matrix-based intra-prediction mode (3200) so as to obtain a prediction vector (518), and predict samples of the predetermined block (18) on the basis of the prediction vector (518), wherein the list (528) of most probable intra-prediction modes is formed on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) of most probable intra-prediction modes is free of the DC intra prediction mode (506) in case of the neighbouring blocks (524, 526) being predicted by any of the angular intra prediction modes (500). 2. Apparatus (3000) of claim 1, configured to if the set-selective syntax element (522) indicates that the predetermined block (18) is to be predicted using one of the first set (508) of intra-prediction modes, derive an MPM syntax element (532) from the data stream (12) which indicates whether the predetermined intra-prediction mode (3100) of the first set (508) of intra- prediction modes is within the list (528) of most probable intra-prediction modes or not, if the MPM syntax element (532) indicates that the predetermined intra-prediction mode (3100) of the first set (508) of intra-prediction modes is within the list (528) of most probable intra-prediction modes, perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, the derivation of the MPM list index (534) from the data stream (12) which points to a predetermined intra-prediction mode (3100) of the list (528) of most probable intra-prediction modes, if the MPM syntax element (532) from the data stream (12) indicates that the predetermined intra-prediction mode (3100) of the first set (508) of intra- prediction modes is not within the list (528) of most probable intra-prediction modes, derive a further list index (536) from the data stream (12) which indicates the predetermined intra-prediction mode (3100) out of the first set (508) of intra- prediction modes. 3. Apparatus (3000) of claim 1 or 2, configured to if the set-selective syntax element (522) indicates that the predetermined block (18) is not to be predicted using one of the first set (508) of intra-prediction modes, derive a further MPM syntax element (538) from the data stream (12) which indicates whether the predetermined block-based intra-prediction mode (3200) of the second set (520) of block-based intra-prediction modes (510) is within a list (542) of most probable block-based intra-prediction modes (510) or not, if the further MPM syntax element (538) indicates that the predetermined block- based intra-prediction mode (3200) of the second set (520) of block-based intra- prediction modes (510) is within a list (542) of most probable block-based intra- prediction modes (510), form the list (542) of most probable block-based intra-prediction modes (510) on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, deriving a further MPM list index (540) from the data stream (12) which points into the list (542) of most probable block-based intra-prediction modes (510) onto the predetermined block-based intra-prediction mode (3200) of, if the further MPM syntax element (538) indicates that the predetermined block- based intra-prediction mode (3200) of the second set (520) of block-based intra- prediction modes (510) is not within a list (542) of most probable block-based intra-prediction modes (510), derive an even further list index (546) from the data stream (12) which indicates the predetermined block-based intra-prediction mode (3200) out of the second set (520) of block-based intra-prediction modes (510). 4. Apparatus (3000) of any of claims 1 to 3, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) is populated with the DC intra-prediction mode (506) only in case of, for each of the neighboring blocks (524, 526), the respective neighbouring block predicted using any of at least one non-angular intra-prediction modes (504, 506) with the first set (508), which comprise the DC intra-prediction mode (506), or predicted using any of block-based intra-prediction modes (510) which, by way of a mapping from the second set (520) of block-based intra-prediction modes (510) onto the intra-prediction modes within the first set (508), which is used for the formation of the list (528) of most probable intra-prediction modes, is mapped onto any of the at least one non-angular intra-prediction modes (504, 506). 5. Apparatus (3000) of any of claims 1 to 4, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that, in case of, for each of the neighboring blocks (524, 526), the respective neighbouring block predicted using any of at least one non-angular intra-prediction modes (504, 506) with the first set (508), which comprise the DC intra-prediction mode (506), or predicted using any of block-based intra-prediction modes (510) which, by way of a mapping from the second set (520) of block-based intra-prediction modes (510) onto the intra-prediction modes within the first set (508), which is used for the formation of the list (528) of most probable intra-prediction modes, is mapped onto any of the at least one non-angular intra-prediction modes (504, 506), the DC intra-prediction mode (506) is positioned before any angular intra-prediction mode (500) in the list (528) of most probable intra-prediction modes. 6. Apparatus (3000) of any of claims 1 to 5, wherein the the first set (508) of intra-prediction modes further comprises a planar intra-prediction mode (504). 7. Apparatus (3000) of any of claims 1 to 6, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) is populated with the planar intra-prediction mode (504) in a manner independent from the intra-prediction modes using which the neighboring blocks (524, 526) are predicted. 8. Apparatus (3000) of claim 7, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the planar intra-prediction mode (504) is positioned at a first position of the list (528) of most-probable intra-prediction modes independent from the intra-prediction modes using which the neighboring blocks (524, 526) are predicted. 9. Apparatus (3000) of any of claims 1 to 8, configured to form a sample value vector (400) out of the plurality of reference samples (17), derive from the sample value vector (400) the vector (514) so that the sample value vector (400) is mapped by a predetermined invertible linear transform (403) onto the vector (514). 10. Apparatus (3000) of claim 9, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is a predetermined value. 11. Apparatus (3000) of claim 10, wherein the predetermined value is one of an average, such as an arithmetic mean or weighted average, of components of the sample value vector (400), a default value, a value signalled in a data stream (12) into which the picture (10) is coded, and a component of the sample value vector (400) corresponding to the predetermined component. 12. Apparatus (3000) of claim 9, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is an arithmetic mean of components of the sample value vector (400). 13. Apparatus (3000) of claim 9, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is a component of the sample value vector (400) corresponding to the predetermined component, wherein the apparatus (3000) is configured to comprise a plurality of invertible linear transforms (403), each of which is associated with one component of the further vector (514, 402), select the predetermined component out of the components of the sample value vector (400) and use the invertible linear transform (403) out of the plurality of invertible linear transforms (403) which is associated with the predetermined component as the predetermined invertible linear transform (403). 14. Apparatus (3000) of any of claims 10 to 13, wherein matrix components of the prediction matrix (516) within a column of the prediction matrix (516) which corresponds to the predetermined component of the further vector (514, 402) are all zero and the apparatus (3000) is configured to compute the matrix-vector product (512) by performing multiplications by computing a matrix vector product (512) between a reduced prediction matrix resulting from the prediction matrix (516) by leaving away the column and an even further vector resulting from the further vector (514, 402) by leaving away the predetermined component. 15. Apparatus (3000) of any of claims 10 to 14, configured to, in predicting the samples of the predetermined block on the basis of the prediction vector (518), compute for each component of the prediction vector (518) a sum of the respective component and a. 16. Apparatus (3000) of any of claims 10 to 15, wherein a matrix, which results from summing each matrix component of the prediction matrix (516) within a column of the prediction matrix (516), which corresponds to the predetermined component of the further vector (514, 402), with one, [i.e. matric B] times the invertible linear transform (403) corresponds to a quantized version of a machine learning prediction matrix. 17. Apparatus (3000) of any of claims 9 to 16, configured to form (100) the sample value vector (400) out of the plurality of reference samples (17) by, for each component of the sample value vector (400), adopting one reference sample of the plurality of reference samples (17) as the respective component of the sample value vector (400), and/or averaging two or more components of the sample value vector (400) to obtain the respective component of the sample value vector (400). 18. Apparatus (3000) of any of claims 1 to 17, wherein the plurality of reference samples (17) are arranged within the picture (10) alongside an outer edge of the predetermined block (18). 19. Apparatus (3000) of any of claims 1 to 18, configured to compute the matrix-vector product (512) using fixed point arithmetic operations. 20. Apparatus (3000) of any of claims 1 to 19, configured to compute the matrix-vector product (512) without floating point arithmetic operations. 21. Apparatus (3000) of any of claims 1 to 20, configured to store a fixed point number representation of the prediction matrix (516). 22. Apparatus (3000) of any of claims 10 to 21, configured to represent the prediction matrix (516) using prediction parameters and to compute the matrix-vector product (512) by performing multiplications and summations on the components of the further vector (514, 402) and the prediction parameters and intermediate results resulting therefrom, wherein absolute values of the prediction parameters are representable by an n-bit fixed point number representation with n being equal to or lower than 14, or, alternatively, 10, or, alternatively, 8. 23. Apparatus (3000) of claim 22, wherein the prediction parameters comprise weights each of which is associated with a corresponding matrix component of the prediction matrix (516). 24. Apparatus (3000) of claim 23, wherein the prediction parameters further comprise one or more scaling factors each of which is associated with one or more corresponding matrix components of the prediction matrix (516) for scaling the weight associated with the one or more corresponding matrix component of the prediction matrix (516), and/or one or more offsets each of which is associated with one or more corresponding matrix components of the prediction matrix (516) for offsetting the weight associated with the one or more corresponding matrix component of the prediction matrix (516). 25. Apparatus (3000) of any of claims 1 to 24, configured to, in predicting the samples of the predetermined block (18) on the basis of the prediction vector (518), use interpolation to compute at least one sample position of the predetermined block (18) based on the prediction vector (518) each component of which is associated with a corresponding position within the predetermined block (18). 26. Apparatus (6000) for encoding a predetermined block (18) of a picture (10) using intra-prediction, configured to signal a set-selective syntax element (522) in a data stream (12) which indicates whether the predetermined block (18) is to be predicted using one of a first set (508) of intra- prediction modes comprising a DC intra prediction mode (506) and angular prediction modes (500), if the set-selective syntax element (522) indicates that the predetermined block (18) is to be predicted using one of the first set (508) of intra-prediction modes, form a list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, signal a MPM list index (534) in the data stream (12) which points into the list (528) of most probable intra-prediction modes onto a predetermined intra-prediction mode (3100), intra-predicting the predetermined block (18) using the predetermined intra-prediction mode (3100), if the set-selective syntax element (522) indicates that the predetermined block (18) is not to be predicted using one of the first set (508) of intra-prediction modes, signal a further index (540; 546) in the data stream (12) which indicates a predetermined matrix-based intra-prediction mode (3200) out of a second set (520) of matrix-based intra-prediction modes (510) by compute a matrix-vector product (512) between a vector (514, 400, 402) derived from reference samples (17) in a neighbourhood of the predetermined block (18) and a predetermined prediction matrix (516) associated with the predetermined matrix-based intra-prediction mode (3200) so as to obtain a prediction vector (518), and predict samples of the predetermined block (18) on the basis of the prediction vector (518), wherein the list (528) of most probable intra-prediction modes is formed on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) of most probable intra-prediction modes is free of the DC intra prediction mode (506) in case of the neighbouring blocks (524, 526) being predicted by any of the angular intra prediction modes (500). 27. Apparatus (6000) of claim 26, configured to if the set-selective syntax element (522) indicates that the predetermined block (18) is to be predicted using one of the first set (508) of intra-prediction modes, signal an MPM syntax element (532) in the data stream (12) which indicates whether the predetermined intra-prediction mode (3100) of the first set (508) of intra-prediction modes is within the list (528) of most probable intra-prediction modes or not, if the MPM syntax element (532) indicates that the predetermined intra-prediction mode (3100) of the first set (508) of intra-prediction modes is within the list (528) of most probable intra-prediction modes, perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, the signalisation of the MPM list index (534) in the data stream (12) which points to a predetermined intra-prediction mode (3100) of the list (528) of most probable intra-prediction modes, if the MPM syntax element (532) in the data stream (12) indicates that the predetermined intra-prediction mode (3100) of the first set (508) of intra-prediction modes is not within the list (528) of most probable intra-prediction modes, signal a further list index (536) in the data stream (12) which indicates the predetermined intra-prediction mode (3100) out of the first set (508) of intra- prediction modes. 28. Apparatus (6000) of claim 26 or 27, configured to if the set-selective syntax element (522) indicates that the predetermined block (18) is not to be predicted using one of the first set (508) of intra-prediction modes, signal a further MPM syntax element (538) in the data stream (12) which indicates whether the predetermined block-based intra-prediction mode (3200) of the second set (520) of block-based intra-prediction modes (510) is within a list (542) of most probable block-based intra-prediction modes (510) or not, if the further MPM syntax element (538) indicates that the predetermined block- based intra-prediction mode (3200) of the second set (520) of block-based intra- prediction modes (510) is within the list (542) of most probable block-based intra- prediction modes (510), form the list (542) of most probable block-based intra-prediction modes (510) on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted, signaling a further MPM list index (540) in the data stream (12) which points into the list (542) of most probable block-based intra-prediction modes (510) onto the predetermined block-based intra-prediction mode (3200) of, if the further MPM syntax element (538) indicates that the predetermined block- based intra-prediction mode (3200) of the second set (520) of block-based intra- prediction modes (510) is not within the list (542) of most probable block-based intra-prediction modes (510), signal an even further list index (546) in the data stream (12) which indicates the predetermined block-based intra-prediction mode (3200) out of the second set (520) of block-based intra-prediction modes (510). 29. Apparatus (6000) of any of claims 26 to 28, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) is populated with the DC intra-prediction mode (506) only in case of, for each of the neighboring blocks (524, 526), the respective neighbouring block predicted using any of at least one non-angular intra-prediction modes (504, 506) with the first set (508), which comprise the DC intra-prediction mode (506), or predicted using any of block-based intra-prediction modes (510) which, by way of a mapping from the second set (520) of block-based intra-prediction modes (510) onto the intra-prediction modes within the first set (508), which is used for the formation of the list (528) of most probable intra-prediction modes, is mapped onto any of the at least one non-angular intra-prediction modes (504, 506). 30. Apparatus (6000) of any of claims 26 to 29, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that, in case of, for each of the neighboring blocks (524, 526), the respective neighbouring block predicted using any of at least one non-angular intra-prediction modes (504, 506) with the first set (508), which comprise the DC intra-prediction mode (506), or predicted using any of block-based intra-prediction modes (510) which, by way of a mapping from the second set (520) of block-based intra-prediction modes (510) onto the intra-prediction modes within the first set (508), which is used for the formation of the list (528) of most probable intra-prediction modes, is mapped onto any of the at least one non-angular intra-prediction modes (504, 506), the DC intra-prediction mode (506) is positioned before any angular intra-prediction mode (500) in the list (528) of most probable intra-prediction modes. 31. Apparatus (6000) of any of claims 26 to 30, wherein the the first set (508) of intra-prediction modes further comprises a planar intra-prediction mode (504). 32. Apparatus (6000) of any of claims 26 to 31, configured to perform the formation of the list (528) of most probable intra-prediction modes bn the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the list (528) is populated with the planar intra-prediction mode (504) in a manner independent from the intra-prediction modes using which the neighboring blocks (524, 526) are predicted. 33. Apparatus (6000) of claim 32, configured to perform the formation of the list (528) of most probable intra-prediction modes on the basis of intra-prediction modes (3050) using which neighbouring blocks (524, 526) neighbouring the predetermined block (18) are predicted such that the planar intra-prediction mode (504) is positioned at a first position of the list (528) of most-probable intra-prediction modes independent from the intra-prediction modes using which the neighboring blocks (524, 526) are predicted. 34. Apparatus (6000) of any of claims 26 to 33, configured to form a sample value vector (400) out of the plurality of reference samples (17), derive from the sample value vector (400) the vector (514) so that the sample value vector (400) is mapped by a predetermined invertible linear transform (403) onto the vector (514). 35. Apparatus (6000) of claim 34, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is a predetermined value. 36. Apparatus (6000) of claim 35, wherein the predetermined value is one of an average, such as an arithmetic mean or weighted average, of components of the sample value vector (400), a default value, a value signalled in a data stream (12) into which the picture (10) is coded, and a component of the sample value vector (400) corresponding to the predetermined component. 37. Apparatus (6000) of claim 34, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is an arithmetic mean of components of the sample value vector (400). 38. Apparatus (6000) of claim 34, wherein the invertible linear transform (403) is defined such that a predetermined component of the further vector (514, 402) becomes a, and each of other components of the further vector (514, 402), except the predetermined component, equal a corresponding component of the sample value vector (400) minus a, wherein a is a component of the sample value vector (400) corresponding to the predetermined component, wherein the apparatus (6000) is configured to comprise a plurality of invertible linear transforms (403), each of which is associated with one component of the further vector (514, 402), select the predetermined component out of the components of the sample value vector (400) and use the invertible linear transform (403) out of the plurality of invertible linear transforms (403) which is associated with the predetermined component as the predetermined invertible linear transform (403). 39. Apparatus (6000) of any of claims 35 to 38, wherein matrix components of the prediction matrix (516) within a column of the prediction matrix (516) which corresponds to the predetermined component of the further vector (514, 402) are all zero and the apparatus (6000) is configured to compute the matrix-vector product (512) by performing multiplications by computing a matrix vector product (512) between a reduced prediction matrix resulting from the prediction matrix (516) by leaving away the column and an even further vector resulting from the further vector (514, 402) by leaving away the predetermined component. 40. Apparatus (6000) of any of claims 35 to 39, configured to, in predicting the samples of the predetermined block (18) on the basis of the prediction vector (518), compute for each component of the prediction vector (518) a sum of the respective component and a. 41. Apparatus (6000) of any of claims 35 to 40, wherein a matrix, which results from summing each matrix component of the prediction matrix (516) within a column of the prediction matrix (516), which corresponds to the predetermined component of the further vector (514, 402), with one, [i.e. matric B] times the invertible linear transform (403) corresponds to a quantized version of a machine learning prediction matrix. 42. Apparatus (6000) of any of claims 34 to 41, configured to form (100) the sample value vector (400) out of the plurality of reference samples (17) by, for each component of the sample value vector (400), adopting one reference sample of the plurality of reference samples (17) as the respective component of the sample value vector (400), and/or averaging two or more components of the sample value vector (400) to obtain the respective component of the sample value vector (400). 43. Apparatus (6000) of any of claims 26 to 42, wherein the plurality of reference samples (17) are arranged within the picture (10) alongside an outer edge of the predetermined block (18). 44. Apparatus (6000) of any of claims 26 to 43, configured to compute the matrix-vector product (512) using fixed point arithmetic operations. 45. Apparatus (6000) of any of claims 26 to 44, configured to compute the matrix-vector product (512) without floating point arithmetic operations. 46. Apparatus (6000) of any of claims 26 to 45, configured to store a fixed point number representation of the prediction matrix (516). 47. Apparatus (6000) of any of claims 26 to 46, configured to represent the prediction matrix (516) using prediction parameters and to compute the matrix-vector product (512) by performing multiplications and summations on the components of the further vector (514, 402) and the prediction parameters and intermediate results resulting therefrom, wherein absolute values of the prediction parameters are representable by an n-bit fixed point number representation with n being equal to or lower than 14, or, alternatively, 10, or, alternatively, 8. 48. Apparatus (6000) of claim 47, wherein the prediction parameters comprise weights each of which is associated with a corresponding matrix component of the prediction matrix (516). 49. Apparatus (6000) of claim 48, whereih the prediction parameters further comprise one or more scaling factors each of which is associated with one or more corresponding matrix components of the prediction matrix (516) for scaling the weight associated with the one or more corresponding matrix component of the prediction matrix (516), and/or one or more offsets each of which is associated with one or more corresponding matrix components of the prediction matrix (516) for offsetting the weight associated with the one or more corresponding matrix component of the prediction matrix (516). 50. Apparatus (6000) of any of claims 26 to 49, configured to, in predicting the samples of the predetermined block (18) on the basis of the prediction vector (518), use interpolation to compute at least one sample position of the predetermined block (18) based on the prediction vector (518) each component of which is associated with a corresponding position within the predetermined block (18). 51. Method (4000) for decoding a predetermined block (18) of a picture using intra-prediction, comprising: Deriving (4100) a set-selective syntax element from the data stream which indicates whether the predetermined block is to be predicted using one of a first set (508) of intra-prediction modes comprising a DC intra prediction mode (506) and angular prediction modes (500), if the set-selective syntax element (522) indicates (4150) that the predetermined block is to be predicted using one of the first set of intra-prediction modes, forming (4200) a list (528) of most probable intra-prediction modes on the basis of intra-prediction modes using which neighbouring blocks (524, 526) neighbouring the predetermined block are predicted, deriving (4300) a MPM list index (534) from the data stream which points into the list of most probable intra-prediction modes onto a predetermined intra-prediction mode, intra-predicting (4400) the predetermined block using the predetermined intra- prediction mode, if the set-selective syntax element indicates (4155) that the predetermined block is not to be predicted using one of the first set of intra-prediction modes, deriving (4250) a further index (540; 546) from the data stream which indicates a predetermined matrix-based intra-prediction mode out of a second set (520) of matrix-based intra-prediction modes (510) by computing (4350) a matrix-vector product (512) between a vector (514) derived from reference samples (17) in a neighbourhood of the predetermined block and a predetermined prediction matrix (516) associated with the predetermined matrix- based intra-prediction mode so as to obtain a prediction vector (518), and predicting (4450) samples of the predetermined block on the basis of the prediction vector, wherein the list of most probable intra-prediction modes is formed on the basis of intra-prediction modes using which neighbouring blocks neighbouring the predetermined block (100) are predicted such that the list of most probable intra-prediction modes is free of the DC intra prediction mode (506) in case of the neighbouring blocks being predicted by any of the angular intra prediction modes. 52. Method (5000) for encoding a predetermined block (18) of a picture using intra-prediction, comprising: signaling (5100) a set-selective syntax element in a data stream which indicates whether the predetermined block is to be predicted using one of a first set (508) of intra-prediction modes comprising a DC intra prediction mode (506) and angular prediction modes (500), if the set-selective syntax element (522) indicates (5150) that the predetermined block is to be predicted using one of the first set of intra-prediction modes, forming (5200) a list (528) of most probable intra-prediction modes on the basis of intra- prediction modes using which neighbouring blocks (524, 526) neighbouring the predetermined block are predicted, signaling (5300) a MPM list index (534) in the data stream which points into the list of most probable intra-prediction modes onto a predetermined intra-prediction mode, intra-predicting (5400) the predetermined block using the predetermined intra-prediction mode, if the set-selective syntax element indicates (5155) that the predetermined block is not to be predicted using one of the first set of intra-prediction modes, signaling (5250) a further index (540; 546) in the data stream which indicates a predetermined matrix-based intra-prediction mode out of a second set (520) of matrix- based intra-prediction modes (510) by computing (5350) a matrix-vector product (512) between a vector (514) derived from reference samples (17) in a neighbourhood of the predetermined block and a predetermined prediction matrix (516) associated with the predetermined matrix-based intra-prediction mode so as to obtain a prediction vector (518), and predicting (5450) samples of the predetermined block on the basis of the prediction vector, wherein the list of most probable intra-prediction modes is formed on the basis of intra-prediction modes using which neighbouring blocks neighbouring the predetermined block (100) are predicted such that the list of most probable intra-prediction modes is free of the DC intra prediction mode (506) in case of the neighbouring blocks being predicted by any of the angular intra prediction modes. 53. Data stream (12) having a picture encoded thereinto using a method (5000) of claim 52. 54. Computer program having a program code for performing, when running on a computer, a method of any of claims 52 to 53.