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Using Four Hypothesis Probability Estimators for CABAC in Versatile Video Coding

Published:23 January 2023Publication History
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Abstract

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).

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 1s
          February 2023
          504 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3572859
          • Editor:
          • Abdulmotaleb El Saddik
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 January 2023
          • Online AM: 12 May 2022
          • Accepted: 1 April 2022
          • Revised: 10 February 2022
          • Received: 9 June 2021
          Published in tomm Volume 19, Issue 1s

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