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

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 representatio...

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Bibliographic Details
Published in:DCC (Los Alamitos, Calif.) pp. 163 - 172
Main Authors: Haase, Paul, Matlage, Stefan, Kirchhoffer, Heiner, Bartnik, Christian, Schwarz, Heiko, Marpe, Detlev, Wiegand, Thomas
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2020
Subjects:
Arithmetic
Encoding
Estimation
Memory management
Probability
Table lookup
Transform coding
Video coding
Video compression
ISSN:2375-0359
Online Access:Get full text
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Summary: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.
ISSN:2375-0359
DOI:10.1109/DCC47342.2020.00024