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.

String prediction (SP) is a very efficient screen content coding (SCC) tool which has been adopted in the third generation of Audio Video Standard (AVS3). It is observed that two special types of strings occur frequently. To further improve the coding efficiency for SCC on top of the original SP, a new variation of SP named Equal-value-string and Copy-above-string based SP (ECSP) is proposed. An ECSP coding unit uses only three types of strings: Equal-value-string, Copy-above-string, and Unpredictable-pixel-string. Compared with the AVS3 reference software HPM9.0 with ECSP disabled, using AVS3 SCC Common Test Condition and YUV 4:2:0 test sequences, the proposed technique achieves an average Y BD-rate reduction of5.54and3.01%for All Intra and Low Delay configurations, respectively, with low additional encoding and decoding complexity. The proposed ECSP has been adopted in the AVS3 standard.

Screen content coding (SCC) is increasingly used in mobile devices, and ultralow coding complexity is required for low power consumption. This article proposes an ultralow complexity string matching approach to SCC. The proposed approach has two essential features: 1) allowing only two types of most effective reference strings that have two most frequently occurring values of offset (i.e., displacement vector) named dual unity offset and 2) fully optimized coding of string matching parameters for maximum coding efficiency. Since at most only two offset values and corresponding reference string positions are allowed for any current string being coded, the optimal reference string searching process is extremely simple and only needs to select the best string from at most two candidates. Moreover, in the proposed approach, the value ranges of string matching parameters, i.e., the string offset vector and string length are very limited, resulting in few bits to code the two parameters and high coding efficiency. Compared with the AVS3 reference software HPM7.0 with IBC disabled, using AVS3 SCC common test condition and YUV test sequences in text and graphics with motion category, the proposed technique achieves Y average BD-rate reduction of 10.8% and 5.8% for all intra (AI) and low-delay B (LDB) configurations, respectively, at ultralow encoding and decoding complexity.