Motion Vector Difference Coding With Shared Context and Reduced Bins
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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 accuracy, leading to suboptimal probability estimation and increased data transmission.
Innovation Solution
Implementing a decoder and encoder that use 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 motion vector differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high number of contexts is provided for coding motion vector differences, then coding precision may be improved, but device complexity increases and probability adaptation fails to perform effectively
Solution Approach 1:
The patent merges the context modeling for horizontal and vertical motion vector difference components into a single shared context. Instead of maintaining separate contexts for each component, the invention combines them into one unified context that adapts probability estimates for both components simultaneously, reducing the total number of contexts while maintaining coding efficiency
Solution Approach 2:
The single context is designed to serve multiple functions by handling both horizontal and vertical motion vector difference components. This universal context model can adapt to different statistical characteristics of both components without requiring separate specialized contexts, thereby reducing complexity while preserving probability estimation accuracy
2Measurement precision
If a high number of contexts is provided for coding motion vector differences, then coding precision may be improved, but loss of time increases due to inspection of neighboring bins
Solution Approach 1:
By merging the context modeling for horizontal and vertical components into a single shared context, the patent eliminates the need to inspect neighboring bins to select between multiple contexts. This unified approach reduces the number of context selection operations and neighboring bin inspections required during decoding, thereby reducing execution time while maintaining probability estimation accuracy
3Device complexity
If the number of contexts is provided too low, then device complexity is reduced, but coding precision deteriorates as bins of highly varying actual symbol statistics are grouped together
Solution Approach 1:
The patent changes the parameter of context granularity by moving from multiple fine-grained contexts to a single unified context. This parameter change is compensated by enhancing the probability adaptation mechanism within the single context, allowing it to effectively handle bins with highly varying actual symbol statistics through dynamic probability estimation rather than through context multiplication
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.