PT Unknown
AU Chengyi Zou
   Shuai Wan
   Marta Mrak
   Marc Gorriz Blanch
   Luis Herranz
   Tiannan Ji
TI Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding
BT 29th IEEE International Conference on Image Processing
PY 2022
DI 10.1109/ICIP46576.2022.9897708
DE Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics
AB In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM.
ER