SYSTEM FOR SCENE BASED VIDEO WATERMARKING AND METHODS THEREOF FIELD OF THE INVENTION [0001] The present invention relates to the field of video processing. In particular, the present invention provides a computer-implemented method, system and computer readable medium for video segmentation and digital watermarking. BACKGROUND OF THE INVENTION [0002] With the advancement of technology, media content have been migrated from analog to digital format. The convergence of networks, devices, and services combined with the technological advancements in digital storage, multimedia compression, and miniaturization of digital cameras has led to an explosive growth of online video content. In addition to the professionally produced video content, user-generated content and content produced by hardcore amateurs are also on the rise. Videos can easily be shared over the Internet using popular video sharing sites such as You Tube® and Yahoo!® Video. Although the user experience is enhanced with the new means of content production, distribution and monetization, it has made illegal reproduction and distribution of digital content easier. Piracy of digital media content is increasing day by day and is a major cause of worry for the digital content owners. [0003] A video is a sequence of scenes and a scene is a sequence of images called frames. Increasing volumes of online digital video content and large amount of information contained within each video make it a challenge to search and retrieve relevant video files from a large collection. Video data management systems aim at reducing this complexity by indexing the video files. Indexing of video content as well as many digital watermarking algorithms require the video to be split into scenes. Scene change detection (hereinafter may be referred to as 'SCD') is used for segmentation of videos into contiguous scenes. SCD is instantly performed by human but vast computational resources and efficient complex algorithms are required to automate this process. Scene change detection in videos is a primary requirement of video processing applications used for the purpose of generating data needed by video data management systems. Scene change detection is a fundamental step in content based video retrieval systems, video annotation systems, video indexing methods and video data management systems. Scene changes in videos can either be gradual or abrupt. Abrupt scene changes can result from editing cuts. Gradual scene changes result from spatial effects such as zoom, camera pan and tilt, dissolve, fade in, fade out or the like. Detection of scene changes effectively depends on finding the similarity or the difference between adjacent frames. SCD usually involves measurement of some differences between successive frames. There are several ways to detect the scenes in a video. Pixel wise difference and histogram based difference are some of the techniques used to measure the inter-frame difference. [0004] The existing scene change detection algorithms can be classified into two groups. One group is compressed domain which consists of algorithms that operate on compressed data and other group is uncompressed domain or pixel domain which consists of algorithms that operate on pixel data. The algorithms in compressed domain operate on compressed data, like algorithms based on Macro blocks in MPEG compressed video, algorithms based on motion characterization and segmentation for detecting scene changes in MPEG compressed video, algorithms based on statistical sequential analysis on compressed bit streams, algorithms based on feature extraction based on motion information and vectors or edges or luminance information. The algorithms in uncompressed domain or pixel domain operate on pixel data directly like algorithms based on color diagrams, algorithms based on color histogram and fuzzy color histogram, algorithms based on edge detection and edge difference examinations, algorithms based on background difference and tracking and object tracking. Efficient segmentation of videos into scenes enables effective management of videos. Also, segmentation of video into scenes can lead to effective watermark embedding. Generally, same watermark is embedded inside the video stream which makes it difficult to maintain the statistical and perceptual invisibility. Embedding a different watermark in different scenes can help in achieving statistical and perceptual invisibility and also makes it difficult for the attacker to extract the watermark. [0005] A number of video watermarking algorithms are proposed by the researchers. These algorithms can be classified into two domains; spatial domain or pixel domain video watermarking and frequency domain or transform domain video watermarking. In spatial domain video watermarking, the watermark is embedded in the video frames by simple addition or bit replacement of selected pixels. These methods are computationally fast but less robust. In frequency domain video watermarking methods, the video frame is transformed and watermark is embedded in the transform coefficients. These methods are robust to common signal processing attacks like compression but require high computational time. [0006] The existing technologies have various limitations. They do not identify the scene change with high precision and recall. The efficiency is low because of high false positive rate and false negative rate. Many algorithms are sensitive to motion of object and camera, like zooming and panning. Luminance variance results in scenes to be incorrectly segmented like in cases of excessive brightness change or flickering. Some algorithms fail in case of scene change involving frames of high motion. Algorithms do not consistently perform in cases like a fade, a dissolve or a wipe. [0007] The existing processes have limitations such as video watermarking based methods are unable to carry large amount of information such as a string containing owner's name, responsible person's name and transaction date reliably and existing video watermarking methods embed same watermark for all the instances of video. Further, existing watermarking methods are not suitable for real time applications as they require high watermark embedding time. Most of the video watermarking algorithms do not embed watermark in real-time and hence, not suitable for real-time applications like on-the-fly video watermarking. This is due to the fact that the watermark embedding is done sequentially. Thus, there lies a need to overcome the limitations of existing technology. The present disclosure proposes computer-implemented methods, systems and computer-readable media, improved methods for detecting scene changes in a video and increasing the efficiency of scene change detection so as to detect scenes in real-time. A scene change detection algorithm is proposed which is capable of detecting the abrupt as well as gradual scene changes. SUMMARY OF THE INVENTION [0008] Aspects of the disclosure relate to a system and methods for video processing. [0009] It is therefore an object of the present disclosure to provide systems and methods for detecting scenes in a video. [0010] It is another object of the present disclosure to provide systems and methods for increasing the efficiency of scene change detection in a video. [0011] It is yet another object of the present disclosure to provide systems and methods for grouping logical scenes of a video. [0012] It is yet another object of the present disclosure to provide systems and methods for embedding and extracting a watermark. [0013] The above as well as additional aspects and advantages of the disclosure will become apparent in the following detailed written description. BRIEF DESCRIPTION OF THE DRAWINGS [0014] Features, aspects, and advantages of the present invention will be better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: [0015] FIG. 1 (PRIOR-ART) is a block diagram of a computing device 100 to which the present disclosure may be applied according to an embodiment of the present disclosure. [0016] FIG. 2 is illustrative of a method 200 for scene change detection using the first pass algorithm. [0017] FIG. 3 is illustrative of a method 300 to detect scene change in a video using the second pass algorithm after the application of the first pass. [0018] FIG. 4 is illustrative of a method 400 to improve the efficiency of scene change detection. [0019] FIG. 5 is illustrative of a method 500 to merge one or more scenes, in parallel. [0020] FIG. 6 is illustrative of a method 600 to determine logical scenes in a video. [0021] FIG. 7 is illustrative of a method 700 to pre-process a watermark to be embedded in a final video. [0022] FIG. 8 (PRIOR-ART) is an embodiment, illustrative of the utility of the present disclosure. [0023] FIG. 9 is illustrative of a method 900 to extract a watermark from a final video. DETAILED DESCRIPTION [0024] Disclosed embodiments provide computer-implemented methods, systems, and computer-readable media for detecting scene changes in a video and increasing the efficiency of scene change detection so as to enable an effective watermarking. The embodiments described herein are related to scene detection algorithms along with embodiments relating to logical grouping of scenes so as to enable an effective watermarking of videos. While the particular embodiments described herein may illustrate the invention in a particular domain, the broad principles behind these embodiments could be applied in other fields of endeavor. To facilitate a clear understanding of the present disclosure, illustrative examples are provided herein which describe certain aspects of the disclosure. However, it is to be appreciated that these illustrations are not meant to limit the scope of the disclosure, and are provided herein to illustrate certain concepts associated with the disclosure. [0025] The following description is full and informative description of the best method and system presently contemplated for carrying out the present disclosure which is known to the inventors at the time of filing the patent application. Of course, many modifications and adaptations will be apparent to those skilled in the relevant arts in view of the following description, accompanied drawings and the appended claims. While the systems and methods described herein are provided with a certain degree of specificity, the present disclosure may be implemented with either greater or lesser specificity, depending on the needs of the user. Further, some of the features of the present disclosure may be used to advantage without the corresponding use of other features described in the following paragraphs. [0026] Any headings used herein are for organizational purposes only and are not meant to limit the scope of the description or the claims. As a preliminary matter, the definition of the term "or" for the purpose of the following discussion and the appended claims is intended to be an inclusive "or". That is, the term "or" is not intended to differentiate between two mutually exclusive alternatives. Rather, the term "or" when employed as a conjunction between two elements is defined as including one element by itself, the other element itself, and combinations and permutations of the elements. For example, a discussion or recitation employing the terminology "A" or "B" includes: "A" by itself, "B" by itself and any combination thereof, such as "AB" and/or "BA." As used herein, the word "may" is used in a permissive sense rather than the mandatory sense. Similarly, the words "include", "including", and "includes" mean including, but not limited to. [0027] It is also to be understood that the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. Preferably, the present disclosure is implemented in software as a program tangibly embodied on a program storage device. The program may be uploaded to, and executed by, a machine comprising any suitable architecture. [0028] One or more of the above-described techniques may be implemented in or involve one or more computer systems. FIG. 1 (PRIOR-ART) is a block diagram of a computing device 100 to which the present disclosure may be applied according to an embodiment of the present disclosure. The system includes at least one processor 102, designed to process instructions, for example computer readable instructions (i.e., code) stored on a storage device 104. By processing instructions, processing device 102 may perform the steps and functions disclosed herein. Storage device 104 may be any type of storage device, for example, but not limited to an optical storage device, a magnetic storage device, a solid state storage device and a non-transitory storage device. The storage device 104 may contain an application 104a which is a set of instructions (i.e. code). Alternatively, instructions may be stored in one or more remote storage devices, for example storage devices accessed over a network or the internet 106. The computing device also includes an operating system and microinstruction code. The various processes and functions described herein may either be part of the microinstruction code or part of the program (or combination thereof) which is executed via the operating system. Computing device 100 additionally may have memory 108, an input controller 110, and an output controller 112 and communication controller 114. A bus (not shown) may operatively couple components of computing device 100, including processor 102, memory 108, storage device 104, input controller 110 output controller 112, and any other devices (e.g., network controllers, sound controllers, etc.). Output controller 112 may be operatively coupled (e.g., via a wired or wireless connection) to a display device (e.g., a monitor, television, mobile device screen, touch-display, etc.) in such a fashion that output controller 112 can transform the display on display device (e.g., in response to modules executed). Input controller 110 may be operatively coupled (e.g., via a wired or wireless connection) to input device (e.g., mouse, keyboard, touch-pad, scroll-ball, touch-display, etc.) in such a fashion that input can be received from a user. The communication controller 114 is coupled to a bus (not shown) and provides a two-way coupling through a network link to the internet 106 that is connected to a local network 116 and operated by an internet service provider (hereinafter referred to as 'ISP') 118 which provides data communication services to the internet. Members or subscribers of social media may be connected to the local network 116. A network link typically provides data communication through one or more networks to other data devices. For example, network link may provide a connection through local network 116 to a host computer, to data equipment operated by an ISP 118. A server 120 may transmit a requested code for an application through internet 106, ISP 118, local network 116 and communication controller 114. Of course, FIG. 1 illustrates computing device 100 with all components as separate devices for ease of identification only. Each of the components may be separate devices (e.g., a personal computer connected by wires to a monitor and mouse), may be integrated in a single device (e.g., a mobile device with a touch-display, such as a smartphone or a tablet), or any combination of devices (e.g., a computing device operatively coupled to a touch-screen display device, a plurality of computing devices attached to a single display device and input device, etc.). Computing device 100 may be one or more servers, for example a farm of networked servers, a clustered server environment, or a cloud network of computing devices. [0029] Generally, the same watermark is embedded inside all the frames of a video. Hence, it is very difficult to maintain statistical and perceptual invisibility in videos. According to an embodiment of the present disclosure, a scene based watermarking method may be applied for both, gradual and abrupt scenes. The proposed method detects the scene change points and embeds independent as well as different segments of the watermark inside the scenes of a video in a cyclic manner. Scene change detection 500 in accordance with the present disclosure may be applied. Alternatively, other known SCD methods can be applied. Once the scenes are detected, segments of the watermark may be embedded inside all the scenes of the video. [0030] FIG. 2 is illustrative of a method 200 for scene change detection using the first pass algorithm. The final video in which the video is to be embedded is read and divided into scenes 202. Suppose the total number of frames in the video to be segmented is N, then total N-l frame difference values are computed and stored. Each frame is partitioned into rectangular blocks of sub-windows (w * w). The frame difference between two consecutive frames, N and N+i is calculated 204 for all frames of the input video using local x2 color histogram comparison. Local maxima in a histogram represents the point of scene change. Peak values are points of local maxima and are identified among all stored N-l frame difference values. A peak value is a frame difference value which is greater than both previous and next frame difference values. There may be a large variation of the frame difference values obtained by applying this method and may be difficult to obtain information about connected frames of a scene. Hence, a sliding window detector is used to extract robust scene changes from frame differences by comparing the frame difference value of the frame corresponding to the point of local maxima 206. The sliding window detector ensures that the number of frames to be considered for frame difference is taken into account. According to an embodiment of the present disclosure, weight for brightness grade change of each color space may be employed to calculate the difference among consecutive frames to make the scene change detection process robust against luminance changes. An appropriate threshold factor, X, is selected. The threshold value may be pre-configured based on one or more experimental values. Preferably, threshold factor ranging between 3-4 may be used. Threshold factor is used to identify those points of local maxima which fulfill the threshold condition. From these points of local maxima, the key frames are identified. If the peak value is greater than X times of average of the differences of the frames under consideration (frames under consideration are the number of frames in the sliding window), then the frame corresponding to this frame difference value is considered as key frame or the reference frame. The term, reference frame corresponds to the first frame of a new scene. Key frame is nothing but the start frame of a new scene. Thus, the first set of scene changes is obtained using first pass of the algorithm 208. [0031] FIG. 3 is illustrative of a method 300 to detect scene change in a video using the second pass algorithm after the application of the first pass. The final video 302 is read and divided into the number of frames 304. First pass is applied 306 to calculate the scenes using the method 200. The scenes detected at the first pass may contain false scenes. The second pass inspects the scenes detected at the first pass and eliminates the false scenes. If the degree of change between adjacent scenes is high, then the scene can be marked as abrupt scene, otherwise it can be either a gradual scene or same scene and is further inspected. The degree of change between two adjacent scenes can be measured by selecting a reference frame. The term 'reference frame' means the key frame obtained in the first pass for each of the identified scenes. Frames to the left 308 mean frames whose number is less than the reference frame. Frames to the right 308 mean frames whose number is greater than the reference frame. A 'left average' (Avgieft) 310 is computed by calculating the #2 color histogram difference between the reference frame and the frames to its left and computing their average. [0032] Ava, ft Equation 1 [0033] Where: [0034] denotes the number of frames to the left of reference frame [0035] denotes the color histogram difference [0036] A 'right average' (AvgTight) 312 is computed by calculating the /2color histogram difference between the reference frame and the frames to the right and computing their average. [0037] A.vgriaht Equation II aright Number of frames DtHkB right M [0038] Where: [0039] £ denotes the number of frames to the right of reference frame [0040] dj denotes the color histogram difference [0041] The logarithmic difference between left average and the right average is calculated 314. This difference can be used to classify a scene as gradual or abrupt. If the difference between the left average and the right average is greater than a first pre-configured threshold then the scene may be classified as an abrupt scene 316. The first pre-configured threshold denotes a threshold for abrupt scenes which may be based on experimental values. If the difference between the left average and the right average is lesser than the first pre-configured threshold and greater than a second pre-configured threshold 318 then the scene may be classified as a gradual scene 320. According to an embodiment of the present disclosure, scenes classified as gradual may be inspected further. A #2 color histogram difference is calculated between the middle frames of the previous scene and the next scene. [0042] diffresrem previous Fumssct rsferenesl fution III [0043] where previous and next represents the first frame of the previous scene and last frame of the next scene. [0044] If the scene is a gradual scene, then the difference (diff) will be too high and behave a an abrupt scene. If the difference between the left and the right average is greater than a third pre-configured threshold 322 then the scene may be classified as a gradual scene 324 else it may be classified as the same scene 326. The second pre-configured threshold and the third pre-configured threshold denotes thresholds for gradual scene which may be based on experimental values. [0045] According to another embodiment of the present invention, the efficiency of scene change detection may be improved. FIG. 4, in combination with FIG. 5 is illustrative of methods 400 and 500 to improve the efficiency of scene change detection and merge scenes in parallel, respectively. The final video 302 in which the watermark is to be embedded is read and divided into frames. Suppose there are N frames in the video. The frames of the video are divided into equal or unequal sets 402 as applicable. W-((i-l)X j-|l ifi = x [0046] Set(i) = I v lxi/ ...Equation IV I—L otherwise [0047] N = {Set(i)},l