This work describes the modification of Context-based Adaptive Binary arithmeticcoding (CABAC) using the double bit range estimation in the VVC engine and the consideration of range updates by using eight hypothetica...
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This work describes the modification of Context-based Adaptive Binary arithmeticcoding (CABAC) using the double bit range estimation in the VVC engine and the consideration of range updates by using eight hypothetical probability estimators. The focus is on the selected adaptation rates performed in these proposed estimators, which are chosen based on memory consideration and coding efficiency. An investigation of arithmetic coding engines with multi-hypothesis probability estimates and their consideration of contextual modeling of entropy coding at the level of transform coefficients. The proposed scheme enables a quantitative representation of probabilistic predictions linearly and describes the scalability potential for higher accuracy. In addition, this work discusses the hardware implementation, which is based on simple operations such as bitwise operations and subinterval updates. The experimental results validate the effectiveness of the proposed approach specified in VTM framework. The improved results show that it provides more significant gains in terms of RA and LD, which is better than the AI configuration.
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