OPEN-SOURCE: ATTENTION-BASED NEURAL NETWORKS FOR CHROMA INTRA PREDICTION IN VIDEO CODING

Neural networks can be successfully used to improve several modules of advanced video coding schemes. In particular, compression of colour components was shown to greatly benefit from usage of machine learning models, thanks to the design of appropriate attention-based architectures that allow the prediction to exploit specific samples in the reference region. However, such architectures tend to be complex and computationally intense, and may be difficult to deploy in a practical video coding pipeline. The software presented in this paper introduces a collection of simplifications to reduce the complexity overhead of the attention-based architectures. The simplified models are integrated into the Versatile Video Coding (VVC) prediction pipeline, retaining compression efficiency of previous chroma intra-prediction methods based on neural networks, while offering different directions for significantly reducing coding complexity.