Motion Vector Difference Coding With Shared Context Binarization
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Solution Overview
Problem
Existing video codecs face inefficiencies in entropy coding of motion vector differences due to high context numbers affecting coding complexity and probability estimation, leading to suboptimal encoding and decoding processes.
Innovation Solution
Implementing a decoder and encoder that use context-adaptive binary entropy decoding and encoding with a truncated unary code and Exp-Golomb code for motion vector differences, 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 and merging concepts 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 coding flexibility and adaptability are improved, but coding complexity and probability estimation accuracy deteriorate
Solution Approach 1:
The patent segments the motion vector difference coding range into multiple intervals (e.g., [−12, −2), [−2, 2), [2, 12)) and assigns different binarization methods to each interval. This segmentation allows the system to maintain high adaptability through multiple contexts while reducing overall complexity by applying simpler coding methods to specific ranges where they are most effective.
Solution Approach 2:
The patent applies different binarization strategies to different intervals of motion vector differences based on their local characteristics. For small magnitude differences, truncated unary code is used; for larger magnitudes, exponential Golomb code is applied. This local optimization improves probability estimation accuracy without requiring a high number of contexts across the entire range.
2Measurement precision
If a high number of contexts is provided for coding motion vector differences, then probability adaptation is improved, but execution efficiency and decoding speed deteriorate
Solution Approach 1:
The patent implements dynamic context selection based on the magnitude of motion vector differences. The system dynamically switches between different binarization methods and contexts depending on the interval in which the motion vector difference falls, rather than using a fixed large number of contexts. This dynamic approach maintains probability estimation accuracy while improving decoding speed by reducing the overhead of context management.
3Ease of manufacture
If the cutoff value for binarization is increased, then coding simplicity is improved, but coding efficiency deteriorates
Solution Approach 1:
The patent optimizes the cutoff value parameter for binarization based on statistical analysis of motion vector difference distributions. By carefully selecting the cutoff value that separates truncated unary code application from exponential Golomb code application, the system achieves an optimal balance between coding simplicity and compression efficiency, preventing both information loss and excessive complexity.
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.