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State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding

2020 DATA COMPRESSION CONFERENCE (DCC 2020)(2020)

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Abstract
In this paper we present a "State-Based Multi-Parameter Probability Estimation" (SBMP) for Context-Based Adaptive Binary Arithmetic Coding (CABAC) which employs a two hypotheses probability estimator based on exponentially weighted moving averages. It uses a logarithmic state representation and a single subsampled transition table with only 32 elements for the probability update. This reduces the memory requirements virtually without affecting the compression efficiency, compared to corresponding approaches that use a linear state representation and a computation-based probability update. The proposed scheme is based on simple operations like table look-ups and additions. Compared to the state-of-the-art probability estimator of the video compression standard H.265/HEVC, the compression efficiency is increased by up to 1 % Bjøntegaard-Delta bit rate (BD rate) when applied to draft 2 of the Versatile Video Coding (VVC) standard. Furthermore, SBMP was recently adopted to working draft 2 of the MPEG-7 part 17 standard for compression of neural networks for multimedia content description and analysis.
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Key words
video compression standard,State-Based Multi-Parameter Probability Estimation,exponentially weighted moving averages,state-of-the-art probability estimator,computation-based probability update,linear state representation,compression efficiency,logarithmic state representation,hypotheses probability estimator,Context-Based Adaptive Binary Arithmetic Coding,State-Based Multiparameter Probability Estimation
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