Motion Vector Difference Entropy Coding With Reduced Contexts
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Solution Overview
Problem
Existing video codecs face inefficiencies in entropy coding of motion vector differences due to high context numbers, which lead to ineffective probability adaptation and increased coding complexity, while low context numbers fail to accurately encode bins with varying symbol statistics.
Innovation Solution
Implementing a decoder and encoder that use context-adaptive binary entropy coding 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 methods to reduce the number of motion vector differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high number of contexts is provided for entropy coding of motion vector differences, then the coding can accommodate bins with varying symbol statistics, but the coding complexity increases and probability adaptation becomes ineffective
Solution Approach 1:
The binarization is segmented into two distinct parts: a truncated unary code for small values (0-1) and an exponential Golomb code for larger values (2 and above). This segmentation allows different coding strategies to be applied to different ranges, reducing the need for numerous contexts while maintaining accuracy for each segment's statistical characteristics
Solution Approach 2:
The invention changes the parameter of cutoff value to 2, which is lower than the conventional value of 9. This parameter change enables the truncated unary code to handle more cases, reducing the number of bins requiring exponential Golomb coding and thereby reducing the number of contexts needed while maintaining effective probability adaptation
2Measurement precision
If a high number of contexts is provided for entropy coding, then bins with varying symbol statistics can be encoded more accurately, but the decoding process becomes more complex due to the need to inspect neighboring bins
Solution Approach 1:
By segmenting the binarization into truncated unary and exponential Golomb parts with a clear cutoff at value 2, the invention creates a straightforward decoding path. The decoder can determine which coding method to use based on the decoded value itself, eliminating the need to inspect neighboring bins to determine the coding method, thus simplifying the decoding process while maintaining encoding accuracy
3Productivity
If the cutoff value is set high (e.g., 9), then more bins use exponential Golomb coding, but this increases the number of contexts needed and reduces coding efficiency
Solution Approach 1:
The invention explicitly changes the cutoff value parameter from the conventional 9 to 2. This parameter change shifts the boundary between truncated unary and exponential Golomb coding, causing more bins to be encoded with the simpler truncated unary method. This reduces the number of contexts required while improving overall coding efficiency by applying the more compact encoding method more frequently
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.