Residual Coefficient Coding for Transform Skip Video Compression
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Solution Overview
Problem
Existing video coding techniques face challenges in efficiently compressing video data while maintaining video quality, particularly in handling residual and coefficient coding, especially in transform skip mode where statistical characteristics of residual signals differ from traditional transform coefficients.
Innovation Solution
The proposed solution involves using variable sets of binary codewords for coding certain syntax elements in transform skip residual coding, determined by specific coded information such as quantization parameters, bit-depth, and flags associated with the block, slice, or sequence level, employing methods like fixed rice parameters, fixed length binarization, truncated Rice binarization, and k-th order Exp-Golomb binarization to optimize coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional transform coding is used, then coding simplicity is maintained, but coding efficiency deteriorates for transform skip mode with different statistical characteristics
Solution Approach 1:
The patent implements dynamic selection of binarization methods (fixed-length, truncated Rice, Exp-Golomb) based on the statistical characteristics of residual signals in transform skip mode. The coding scheme adapts to different signal types by selecting appropriate binarization strategies, allowing the system to optimize coding efficiency for each specific case rather than using a fixed approach.
Solution Approach 2:
The patent changes coding parameters (binarization method, rice parameter values) based on the statistical properties of the residual signal. By monitoring signal characteristics and adjusting coding parameters accordingly, the system achieves better compression performance for transform skip mode while maintaining compatibility with traditional coding paths.
2Productivity
If fixed binarization method is used, then device complexity is reduced, but coding efficiency deteriorates for different signal characteristics
Solution Approach 1:
The patent employs dynamic binarization method selection where the coding scheme transitions from fixed to adaptive based on signal characteristics. Different binarization methods (fixed-length, truncated Rice, Exp-Golomb) are selected dynamically according to the statistical properties of the residual signal, enabling optimal compression performance for each signal type.
Solution Approach 2:
The patent applies different binarization methods to different parts of the coding process based on local signal characteristics. Instead of using a uniform approach, the system tailors the binarization strategy to the specific statistical properties of each residual signal segment, achieving locally optimized compression performance.
3Productivity
If adaptive coding is used for different signal characteristics, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary classification of signal characteristics before applying specific binarization methods. By pre-identifying the statistical properties of the residual signal and selecting the appropriate coding strategy in advance, the system avoids complex real-time adaptations during decoding, thereby reducing decoder complexity while maintaining coding efficiency.
Data Source
AI summary
Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video decoding. In one method, a decoder determines a coding bit depth for at least one sample in a bitstream; the decoder determines a value of a first Sequence Parameter Set (SPS) flag for the at least one sample; and the decoder further determines a second SPS flag for the at least one sample based on the value of the first SPS flag in combination of a coding bit depth for the at least one sample.