A new approach to entropy coding that utilizes the time-varying statistics of the symbols of a video codec and statistical dependencies between them for bit rate reduction.

Natural camera-view video signals show non-stationary statistical behavior. The statistics of these signals largely depend on the video content and the acquisition process. Traditional concepts of video coding that rely on a mapping from the video signal to a bitstream of variable length-coded syntax elements exploit some of the non-stationary characteristics but certainly not all of it. Moreover, higher-order statistical dependencies on a syntax element level are mostly neglected in existing video coding schemes. Designing an entropy coding scheme for a video coder by taking into consideration these typically observed statistical properties, however, offers room for significant improvements in coding efficiency as achieved with Context-based Adaptive Binary Arithmetic Coding (CABAC).

CABAC is a normative part of the new ITU-T | ISO/IEC standard H.264/AVC for video compression and have been developed by ICG. By combining an adaptive binary arithmetic coding technique with context modeling, a high degree of adaptation and redundancy reduction is achieved. The CABAC framework also includes a novel low-complexity method for binary arithmetic coding and probability estimation that is well suited for efficient hardware and software implementations. CABAC significantly outperforms the baseline entropy coding method of H.264/AVC for the typical area of envisaged target applications. For a set of test sequences representing typical material used in broadcast applications and for a range of acceptable video quality of about 30 to 38 dB, average bit-rate savings of 9 to 14% are achieved.

• Usage of adaptive probability models for most symbols
• Exploiting symbol correlations by using contexts
• Restriction to binary arithmetic coding
• Simple and fast adaptation mechanism
• Fast binary arithmetic codec based on table look-ups and shifts only