Concepts For Coding Neural Networks Parameters

Abstract: Embodiments according to a first aspect of the present invention are based on the idea, that neural network parameters may be compressed more efficiently by using a non-constant quantizer, but varying same during coding the neural network parameters, namely by selecting a set of reconstruction levels depending on quantization indices decoded from, or respectively encoded, into the data stream for previous or respectively previously encoded neural network parameters. Embodiments according to a second aspect of the present invention are based on the idea that a more efficient neural network coding may be achieved when done in stages - called reconstruction layers to distinguish them from the layered composition of the neural network in neural layers - and if the parametrizations provided in these stages are then, neural network parameter-wise combined to yield a neural network parametrization improved compared to any of the stages.

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