Using Four Hypothesis Probability Estimators for CABAC in Versatile Video Coding

This article introduces the key technologies involved in four hypothetical probability estimators for Context-based Adaptive Binary Arithmetic Coding (CABAC). The focus is on the selected adaptation rate performed in these estimators, which are selected based on coding efficiency and memory considerations, and also the relationship with the current size of the coding block. The proposed scheme can linearly realize the quantitative representation of probabilistic prediction and describes the scalability potential for higher accuracy. Besides a description of the design concept, this work also discusses motivation and implementation aspects, which are based on simple operations such as bitwise operations and single subsampling for subinterval updates. The experimental results verify the effectiveness of the proposed CABAC method specified in Versatile Video Coding (VVC).

Automated summary

This article introduces the key technologies involved in four hypothetical probability estimators for Context-based Adaptive Binary Arithmetic Coding (CABAC). The focus is on the selected adaptation rate performed in these estimators, which are selected based on coding efficiency and memory considerations, and also the relationship with the current size of the coding block. The proposed scheme can linearly realize the quantitative representation of probabilistic prediction and describes the scalability potential for higher accuracy. Besides a description of the design concept, this work also discusses motivation and implementation aspects, which are based on simple operations such as bitwise operations and single subsampling for subinterval updates. The experimental results verify the effectiveness of the proposed CABAC method specified in Versatile Video Coding (VVC).