基于轻量级全连接网络的H.266/VVC分量间预测

Cross-component linear model (CCLM) prediction in H.266/versatile video coding (VVC) can improve the compression efficiency. There exists high correlation between luma and chroma components while the correlation is difficult to be modeled explicitly. An algorithm for neural network based cross-component prediction (NNCCP) was proposed where reference pixels with high correlation were selected according to the luma difference between the reference pixels and the pixel to be predicted. Based on the high-correlated reference pixels and the luma difference, the predicted chroma was obtained based on lightweight fully connected networks. Experimental results demonstrate that the proposed algorithm can achieve 0.27%, 1.54%, and 1.84% bitrate savings for luma and chroma components, compared with the VVC test model 10.0 (VTM10.0). Besides, a unified network can be employed to blocks with different sizes and different quantization parameters.