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- W3013660383 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 % 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." @default.
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- W3013660383 date "2020-03-01" @default.
- W3013660383 modified "2023-09-27" @default.
- W3013660383 title "State-Based Multi-parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding" @default.
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- W3013660383 doi "https://doi.org/10.1109/dcc47342.2020.00024" @default.
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