Neural network based filter in video coding

Abstract

In one implementation, to perform in-loop filtering of a version of reconstructed samples of a block, only a single offset parameter is signaled in the bitstream. Based on the version of reconstructed samples, a pixel-wise weight mask is generated using a neural network. Because the neural network parameters are known at both the encoder and decoder, these parameters need not to be signaled in the bitstream. The single offset parameter scaled by the weighted mask is used to adjust the samples in the block. Thus, even though only a single offset parameter is used, the samples are adjusted by pixel-wise offsets. The neural network may also take other parameters, such as quantization parameters and picture types as input. Further, there can be multiple neural networks that generate different weight masks, where different offsets are signaled and one or more of the neural networks are to be selected for filtering.