Motion Vector Difference Coding With Shared Context Bins
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Solution Overview
Problem
Current video coding techniques face inefficiencies in entropy coding of motion vector differences due to high context numbers, leading to ineffective probability adaptation and increased coding complexity, as well as inadequate encoding of bins with varying symbol statistics.
Innovation Solution
A decoder and encoder system that uses context-adaptive binary entropy decoding and encoding with a truncated unary code and exponential Golomb code, reducing the cutoff value to two bin positions and using a single context for both horizontal and vertical components, along with advanced motion vector prediction methods to reduce the number of motion vector differences transmitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a high number of contexts is provided for coding motion vector differences, then the adaptability to different symbol statistics is improved, but the device complexity and coding complexity increase
Solution Approach 1:
The patent segments the motion vector difference coding into two distinct parts: a truncated unary code for small values (below cutoff) and an exponential Golomb code for larger values (at and above cutoff). This segmentation allows each part to use appropriate coding strategies, reducing the need for numerous contexts while maintaining adaptability to different symbol statistics
Solution Approach 2:
The patent changes the parameter of cutoff value to a small integer (typically 0, 1, or 2), which fundamentally alters the coding structure. By using a small cutoff value, most motion vector differences are encoded with the truncated unary code, requiring minimal contexts, while still allowing exponential Golomb coding for larger values when needed
2Adaptability or versatility
If a high number of contexts is provided for coding motion vector differences, then the adaptability to different symbol statistics is improved, but the probability adaptation effectiveness deteriorates
Solution Approach 1:
The patent segments the coding into truncated unary and exponential Golomb parts with a small cutoff value, creating distinct coding regions that can be adapted to different symbol statistics without requiring numerous contexts. Each segment can be probability-adapted independently with fewer contexts
Solution Approach 2:
The patent applies partial action by providing contexts only for the exponential Golomb code part (for values at and above cutoff), while the truncated unary code part uses a single context or no context. This partial contextualization is sufficient because the small cutoff value ensures most values are coded without requiring multiple contexts
3Device complexity
If the number of contexts is reduced, then the device complexity is reduced, but the adaptability to varying symbol statistics deteriorates
Solution Approach 1:
The patent changes the cutoff parameter to a small value (0, 1, or 2), which fundamentally improves adaptability with minimal contexts. This parameter change ensures that the majority of motion vector differences (those with small absolute values) are coded using the truncated unary code, which requires minimal or no contextual adaptation, while still allowing accurate coding of larger values through exponential Golomb code when needed
4Adaptability or versatility
If multiple contexts are provided for certain bins, then the adaptability to different symbol statistics is improved, but the decoding process complexity increases due to inspection of neighboring bins
Solution Approach 1:
The patent segments the binarization into truncated unary (for small values) and exponential Golomb (for larger values), with contexts provided only for the exponential Golomb part. This segmentation simplifies decoding because the truncated unary part, which constitutes most of the coding, requires no complex context inspection
Solution Approach 2:
By setting the cutoff parameter to a small value (0, 1, or 2), the patent ensures that only a minimal number of bins in the exponential Golomb section require contextual adaptation. This dramatically reduces the decoding complexity compared to providing multiple contexts for all bins, while still maintaining adaptability for the bins that need it
Data Source
AI summary
An entropy decoder is configured to, for horizontal and vertical components of motion vector differences, derive a 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 horizontal and vertical components of the motion vector differences, and an 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 a video based on the integer values of the horizontal and vertical components of the motion vector differences.