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State-based multi-parameter probability estimation for context-based adaptive binary arithmetic coding

: Haase, P.; Matlage, S.; Kirchhoffer, H.; Bartnik, C.; Schwarz, H.; Marpe, D.; Wiegand, T.


Bilgin, A. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
Data Compression Conference, DCC 2020. Proceedings : 24-27 March 2020, Snowbird, Utah, USA, Virtual Conference
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2020
ISBN: 978-1-7281-6457-1
ISBN: 978-1-7281-6458-8
Data Compression Conference (DCC) <2020, Online>
Fraunhofer HHI ()

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 % Bjontegaard-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.