Residual Coding Stage Segmentation for CABAC Efficiency
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Solution Overview
Problem
The existing coding efficiency in modern image and video coding solutions, such as HEVC and VVC, is limited by the effectiveness of Context-Adaptive Binary Arithmetic Coding (CABAC), where applying context modeling to more bins does not significantly increase coding efficiency due to statistical dependencies and computational complexity.
Innovation Solution
Incorporating the encoding and decoding of a quantization error compensation signal alongside the quantized prediction residual, allowing for different binarizations and context modeling to improve coding efficiency by exploiting statistical dependencies in CABAC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context modeling is applied to more bins in CABAC, then coding efficiency should improve, but statistical dependencies and computational complexity limit the effectiveness
Solution Approach 1:
The residual signal is divided into two separate components: quantized prediction residual and quantization error compensation signal. Each component is encoded independently with its own binarization and context modeling strategy, allowing optimized handling of different signal characteristics without overwhelming computational complexity
Solution Approach 2:
Different binarization parameters and context modeling parameters are applied to different signal components. The quantized prediction residual uses one set of parameters while the quantization error compensation signal uses another set, optimizing coding efficiency for each component's statistical properties
2Productivity
If quantization error compensation is added to improve coding efficiency, then bitstream compression improves, but device complexity increases
Solution Approach 1:
The encoding process is segmented into two independent stages: encoding the quantized prediction residual and encoding the quantization error compensation signal. This segmentation allows each stage to be optimized separately and processed through existing CABAC infrastructure with minimal additional complexity
Solution Approach 2:
The quantization error compensation signal acts as an intermediary that bridges the gap between quantized approximation and original signal. It compensates for quantization losses without requiring complete re-encoding of the original residual, thus improving compression while adding manageable complexity
Data Source
AI summary
Decoder for decoding a residual signal from a data stream, configured to decode, from the data stream, a quantized prediction residual and at least one quantization error compensation signal; scale the quantized prediction residual to determine a scaled prediction residual; determine a reconstructed prediction residual based on the scaled prediction residual and the at least one quantization error compensation signal.