Motion Vector Difference Entropy Coding With Fewer Contexts
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Solution Overview
Problem
Current entropy coding methods for video data, particularly in motion vector differences, face inefficiencies due to high context numbers leading to ineffective probability adaptation and increased coding complexity, as well as insufficient context classification resulting in poor encoding of bins with varying statistics.
Innovation Solution
Reducing the cutoff value for truncated unary coding to two bin positions, using a single context for both horizontal and vertical components, and employing advanced motion vector prediction techniques, such as multiple predictors and merging of motion vector differences, to enhance coding efficiency and reduce the number of transmitted differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high number of contexts is provided for coding motion vector differences, then the classification of bins with varying statistics is improved, but the coding complexity increases and probability adaptation becomes ineffective
Solution Approach 1:
The patent segments the binarization process into two distinct parts: truncated unary code for small motion vector differences (0-2) and exponential Golomb code for larger values (3+). This segmentation allows each coding method to be optimized independently, with the truncated unary code using a single context and the exponential Golomb code using multiple contexts, thereby reducing overall complexity while maintaining classification precision.
Solution Approach 2:
The patent changes the cutoff parameter from 9 to 2, fundamentally altering the structure of the binarization process. This parameter change reduces the number of bins requiring complex context selection, thereby reducing coding complexity while improving probability adaptation effectiveness by ensuring sufficient visitation frequency for the reduced number of contexts.
2Measurement precision
If a high number of contexts is provided for coding motion vector differences, then the classification of bins with varying statistics is improved, but the probability adaptation fails to perform effectively
Solution Approach 1:
By changing the cutoff parameter from 9 to 2, the patent ensures that the truncated unary code portion (bins 0, 1, 2) is handled with a single context that receives frequent updates, enabling effective probability adaptation. The exponential Golomb code portion (bins 3+) uses multiple contexts but with reduced frequency requirements, balancing classification precision with adaptation reliability.
3Device complexity
If the cutoff value is reduced to two bin positions, then the coding complexity is reduced and probability adaptation is enhanced, but the classification capability for bins with varying statistics may be insufficient
Solution Approach 1:
The patent segments the coding strategy into two parts: for bins 0-2, a single context is used with truncated unary code, achieving low complexity and effective probability adaptation; for bins 3+, multiple contexts are used with exponential Golomb code, maintaining classification precision for varying statistics. This segmentation resolves the contradiction by applying different strategies to different ranges.
Data Source
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AI summary
A decoder for decoding a video from a data stream into which horizontal and vertical components of motion vector differences are coded using binarizations of the horizontal and vertical components is described, the binarizations equaling a truncated unary code of the horizontal and vertical components, respectively, within a first interval of the domain of the horizontal and vertical components below a cutoff value, and a combination of a prefix in form of the truncated unary code for the cutoff value and a suffix in form of a Exp-Golomb code of the horizontal and vertical components, respectively, within a second interval of the domain of the horizontal and vertical components inclusive and above the cutoff value, wherein the cutoff value is two and the Exp-Golomb code has order one. An entropy decoder is configured to, for the horizontal and vertical components of the motion vector differences, derive the truncated unary code from the data stream using context- adaptive binary entropy decoding with exactly one context per bin position of the truncated unary code, which is common for the horizontal and vertical components of the motion vector differences, and the Exp-Golomb code using a constant equi-probability bypass mode to obtain the binarizations of the motion vector differences. A desymbolizer is configured to debinarize the binarizations of the motion vector difference syntax elements to obtain integer values of the horizontal and vertical components of the motion vector differences; A reconstructor is configured to reconstruct the video based on the integer values of the horizontal and vertical components of the motion vector differences.