Efficient Screen Content Coding Based on Convolutional Neural Network Guided by a Large-Scale Database

Screen content videos (SCVs) are becoming popular in many applications. Compared with natural content videos (NCVs), the SCVs have different characteristics. Therefore, the screen content coding (SCC) based on HEVC adopts some new coding tools (intra block copy and palette mode etc.) to improve coding efficiency, but these tools increase the computational complexity as well. In this paper, we propose to predict the CU partition of the SCVs by a convolutional neural network (CNN) which is trained by the large-scale database that we firstly established for screen content coding. The proposed approach is implemented in SCC reference software SCM-6.1. Experimental results show that our proposed approach can save 53.2% encoding time with 2.67% BD-rate increase on average in All Intra (AI) configurations.