Ultra-Fast Intra Screen Content Coding via Accelerated Re-Visit CU-Coding in AVS3
Xueyan Cao, Tao Lin, Liping Zhao, Wei Han, Shanshe Wang, Kailun Zhou, Yufen Yang · ACM Transactions on Multimedia Computing Communications and Applications · 2025
Screen Content Coding (SCC) is an indispensable tool for enabling distributed collaboration, such as video conferencing. Encoders in the latest video coding standards, particularly for SCC scenarios, employ a wider variety of partitioning tree splitting types, recursively traversing all branches, as well as a larger number of coding modes and submodes, to achieve higher coding efficiency compared to encoders in previous standards. This process leads to very high coding complexity, as each tree leaf node, called a coding unit (CU), for every partitioning size and location in the picture is repeatedly visited and evaluated multiple times during the optimal partitioning search. Additionally, each CU visit involves evaluating a vast number of coding options and their combinations to identify the best one. The complexity is further exacerbated in SCC due to the addition of many new CU coding modes and options. To significantly reduce SCC complexity without coding efficiency loss, this article proposes a new technique, Accelerated Revisit CU-coding (ARC), along with an SCC search space analysis for in-depth operation-level and run/platform-independent assessment of SCC complexity. ARC exploits the correlation between the first visit and subsequent revisits of a CU with the same location and size. By fully leveraging the correlation and information from the first visit, ARC significantly accelerates revisit CU-coding while maintaining the same high coding efficiency. ARC is implemented in HPM, the AVS3 reference software. Experiments demonstrate that ARC reduces encoding runtime by 29.74%, 47.78%, and 54.25% for 1,920 × 1,080 FHD, 4K UHD, and 8K UHD test sequences, respectively, in All Intra configuration, without coding efficiency loss. These runtime reductions align with corresponding search space reductions of 30.91%, 49.67%, and 54.41%, as obtained from the search space analysis.