Context-Adaptive Binary Arithmetic Coding with Dynamic Probability Updates

The patent applies dynamics by transitioning from a fixed probability update model to an adaptive model that dynamically adjusts probability estimates based on actual symbol occurrences. The system switches between different probability update models (e.g., from a first probability update model to a second probability update model) depending on the coding context and observed statistics, enabling both stability and fast convergence.

The patent changes parameters by modifying probability estimates based on observed symbol occurrences. Instead of using fixed probability values, the system updates probability parameters adaptively - for example, adjusting the probability of a symbol based on its recent occurrence frequency. This parameter adaptation allows the system to converge quickly to accurate probability models while maintaining stability through controlled update mechanisms.

The system dynamically selects between different probability update models based on coding context. Rather than using a single fixed model, the decoder adapts its behavior by switching between models depending on factors like symbol type, position in bitstream, and observed statistics. This dynamic adaptation enables the system to handle time-varying occurrence probabilities effectively.

The patent incorporates feedback mechanisms where the system continuously monitors actual symbol occurrences and uses this information to adjust probability estimates. The feedback loop allows the system to detect deviations from expected probability patterns and adapt accordingly, improving adaptability to changing statistical characteristics without requiring overly complex predetermined tables.

The patent segments the probability update process into multiple distinct models, each optimized for specific coding contexts. Instead of using a single monolithic model, the system divides the update process into separate models that can be selectively applied - for example, different models for different symbol types or positional contexts. This segmentation allows each model to be simpler while the overall system achieves higher compression efficiency through optimal model selection.

The system applies local quality by tailoring probability update behavior to specific local contexts within the bitstream. Different regions or types of syntax elements receive customized probability updates based on their specific characteristics. This localized adaptation improves compression efficiency for each context while keeping individual model implementations relatively simple.