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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Statebased multiparameter probability estimation for contextbased adaptive binary arithmetic coding
 Bilgin, A. ; Institute of Electrical and Electronics Engineers IEEE; IEEE Signal Processing Society: Data Compression Conference, DCC 2020. Proceedings : 2427 March 2020, Snowbird, Utah, USA, Virtual Conference Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2020 ISBN: 9781728164571 ISBN: 9781728164588 pp.163172 
 Data Compression Conference (DCC) <2020, Online> 

 English 
 Conference Paper 
 Fraunhofer HHI () 
Abstract
In this paper we present a 'StateBased MultiParameter Probability Estimation' (SBMP) for ContextBased 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 computationbased probability update. The proposed scheme is based on simple operations like table lookups and additions. Compared to the stateoftheart probability estimator of the video compression standard H.265/HEVC, the compression efficiency is increased by up to 1 % BjontegaardDelta 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 MPEG7 part 17 standard for compression of neural networks for multimedia content description and analysis.