2012/10/01 by Vivienne Sze, Madhukar Budagavi · 198 citations
Computer Science · Mathematics · #Video Coding and Compression Technologies #Advanced Vision and Imaging #Image and Video Quality Assessment #Context-adaptive binary arithmetic coding #Context-adaptive variable-length coding #Computer science #Coding tree unit #Coding (social sciences) #Entropy encoding #Algorithmic efficiency #Arithmetic coding #Throughput #Data compression #Algorithm #Computer engineering #Decoding methods #Mathematics #Operating system
paper · doi:10.1109/tcsvt.2012.2221526
published in IEEE Transactions on Circuits and Systems for Video Technology 22(12), 1778-1791 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2012/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Context-adaptive binary arithmetic coding (CAB-AC) is a method of entropy coding first introduced in H.264/AVC and now used in the newest standard High Efficiency Video Coding (HEVC). While it provides high coding efficiency, the data dependencies in H.264/AVC CABAC make it challenging to parallelize and thus, limit its throughput. Accordingly, during the standardization of entropy coding for HEVC, both coding efficiency and throughput were considered. This paper highlights the key techniques that were used to enable HEVC to potentially achieve higher throughput while delivering coding gains relative to H.264/AVC. These techniques include reducing context coded bins, grouping bypass bins, grouping bins with the same context, reducing context selection dependencies, reducing total bins, and reducing parsing dependencies. It also describes reductions to memory requirements that benefit both throughput and implementation costs. Proposed and adopted techniques up to draft international standard (test model HM-8.0) are discussed. In addition, analysis and simulation results are provided to quantify the throughput improvements and memory reduction compared with H.264/AVC. In HEVC, the maximum number of context-coded bins is reduced by 8×, and the context memory and line buffer are reduced by 3× and 20×, respectively. This paper illustrates that accounting for implementation cost when designing video coding algorithms can result in a design that enables higher processing speed and lowers hardware costs, while still delivering high coding efficiency.