Our paper, "An HEVC-Compliant Fast Screen Content Transcoding Framework Based on Mode Mapping", has been accepted by the IEEE Trans. Circuits and Systems for Video Technology. Given the observations that HEVC compliant hardware decoders (but not SCC) are getting popular in recent mobile platforms, and screen content coding (SCC) indeed bings the significant bit rate reduction (50%) at the same quality for screen applications, we had proposed this solution to fast and accurately map the SCC coding mode to a HEVC compliant mode, with a large amount complexity reduction at a negligible performance sacrifice. This work can be implemented at edges for emerging screen applications, such as collaborative screen sharing, cloud gaming, etc.
Abstract:
This paper presents a novel fast transcoding framework to efficiently bridge the state-of-art High Efficiency
Video Coding (HEVC) standard and its Screen Content Coding (SCC) extension to support the bitstream compatibility over the legacy HEVC devices. By exploiting the side information from the SCC bitstream, fast mode and partition decisions are made to accurately translate the novel SCC modes to conventional HEVC modes based on statistical mode mapping techniques. Compared with the “Full-Decoding-Full-Encoding” (FDFE) solution, the proposed framework achieves on average 51% and 82% complexity reductions with 0.57% Bjøntegaard-Delta Rate (BD-Rate) loss and 9.74% BD-Rate gain under All-Intra (AI) and Low-Delay (LD) configurations, respectively. Compared with the direct transcoding reusing Intra mode and Inter motion, the proposed mode mapping framework introduces additional 23% and 6% complexity reductions for AI and LD encoding configurations with 0.43% BD-Rate loss and 1.10% BD-Rate saving, respectively. The proposed solution is extended to support the Single-Input-Multiple-Output (SIMO) screen content adaptive streaming at the edge clouds, where a SCC bitstream coded in high quality is transcoded into multiple HEVC bitstreams in reduced qualities. Our proposed solution achieves on average 49% and 76% complexity reductions with 0.78% BD-Rate loss and 7.40% BD-Rate gain under AI and LD configurations, respectively.