Efficient CU and PU Decision Based on Neural Network and Gray Level Co-Occurrence Matrix for Intra Prediction of Screen Content Coding

Abstract

Screen content coding (SCC) significantly improves the screen content compression efficiency over the high efficient video coding but at the cost of extremely huge computational complexity. The flexible quad-tree coding unit (CU) partitioning structure and the newly-introduced SCC intra modes are largely responsible for the high computational complexity. In order to meet the challenge of huge computational complexity of SCC, we propose an efficient CU and prediction unit (PU) decision based on neural network (NN) and gray level co-occurrence matrix (GLCM) for SCC intra prediction. The proposed efficient SCC intra prediction algorithm contains three stages, including NN-based CU classification model, efficient PU mode decision based on classification (EPMD), and efficient CU size decision based on GLCM and spatiotemporal correlations (ECSD). Consequently, the computational complexity of SCC intra prediction can be drastically reduced by replacing the brute-force search with EPMD and ECSD to decide the optimal combination of CU size and PU mode. In addition, an online updating method of weighted factor is introduced to cope with different characteristics of test sequences. In order to achieve a good tradeoff between complexity reduction and rate distortion (RD) performance, extensive experiments are conducted to select the optimal threshold for ECSD. The experimental results show that in comparison with the original SCC reference software, the proposed algorithm can reduce 49.33% intra coding time with 1.36% BDBR increase and 0.13 dB BDPSNR decrease. Meanwhile, the proposed algorithm outperforms eight state-of-the-art algorithms in terms of computational complexity reduction and RD performance.