AV1 Video Coding Using Texture Analysis With Convolutional Neural Networks

Modern video codecs including the newly developed AOM/AV1 utilize hybrid coding techniques to remove spatial and temporal redundancy. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result. One interesting approach is to only encode visually relevant information and use a different coding method for "perceptually insignificant" regions in the frame, which can lead to substantial data rate reductions while maintaining visual quality. In this paper, we introduce a texture analyzer before encoding the input sequences to identify detail irrelevant texture regions in the frame using convolutional neural networks. We designed and developed a new coding tool referred to as texture mode for AV1, where if texture mode is selected at the encoder, no inter-frame prediction is performed for the identified texture regions. Instead, displacement of the entire region is modeled by just one set of motion parameters. Therefore, only the model parameters are transmitted to the decoder for reconstructing the texture regions. Non-texture regions in the frame are coded conventionally. We show that for many standard test sets, the proposed method achieved significant data rate reductions.