A novel screen content fast transcoding framework based on statistical study and machine learning

Abstract

In this paper, a novel screen content transcoding framework is presented to efficiently bridge the state-of-art High Efficiency Video Coding (HEVC) standard and its incoming screen content coding (SCC) extension currently pending finalization. The proposed scheme is implemented as an Intra-coding “pre-processing” module on top of official SCC test model software (SCM). Both Coding Unit (CU) statistical features (such as CU color quantity, CU pixel variance, CU edge directionality distribution, etc.) and decoded video side information (such as CU partitions, modes, residual, etc.) are jointly analyzed. Accordingly, fast CU mode decisions and CU partitions bypass / termination heuristics are designed. Compared with SCM-4.0 official release, the proposed fast transcoding scheme can achieve an average of 48% re-encoding complexity reduction over JCT-VC screen content testing sequences with less than 2.14% marginal BD-Rate increase under SCC common testing conditions for All-Intra (AI) configuration.