TY  - CONF
AU  - Chengyi Zou
AU  - Shuai Wan
AU  - Marta Mrak
AU  - Marc Gorriz Blanch
AU  - Luis Herranz
AU  - Tiannan Ji
A2  - ICIP
PY  - 2022//
TI  - Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding
BT  - 29th IEEE International Conference on Image Processing
KW  - Video coding
KW  - Quantization (signal)
KW  - Computational modeling
KW  - Neural networks
KW  - Predictive models
KW  - Video compression
KW  - Syntactics
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9897708
UR  - http://dx.doi.org/10.1109/ICIP46576.2022.9897708
N1  - MACO
ID  - Chengyi Zou2022
ER  -