Delta Quantization Parameter Binarization for Video Decoding
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Solution Overview
Problem
The existing image quantization parameter encoders using context-based adaptive binary arithmetic coding face issues in encoding significant delta quantization parameters due to inability to distinguish between positive and negative information, leading to redundant bins and increased data complexity.
Innovation Solution
The proposed solution involves a binarization process that separates information about the significance, absolute value, and sign of the delta quantization parameter, using appropriate contexts and reducing redundancy by leveraging the defined DQP range, resulting in a more efficient encoding and decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the typical quantization parameter encoder uses a unified binarization method without distinguishing positive and negative DQP, then the encoding process is simple, but the encoding efficiency deteriorates due to redundant bins
Solution Approach 1:
The patent segments the binarization process into two distinct paths: one for positive DQP values and one for negative DQP values. This segmentation allows each path to use optimized binarization rules specific to its sign, reducing redundant bins and improving encoding efficiency while maintaining process clarity through structured separation
2Reliability
If the bin string includes redundant bins to cover all possible DQP values, then the decoding coverage is complete, but the data complexity increases
Solution Approach 1:
The patent applies asymmetric binarization rules tailored to the asymmetric distribution of positive and negative DQP values. By designing different binarization patterns for positive and negative values based on their respective probability distributions and ranges, the system achieves complete decoding coverage while minimizing data complexity through optimized, non-uniform bin representation
3Measurement precision
If the number of bins in the bin string is increased to represent all DQP values explicitly, then the precision of DQP representation is improved, but the processing speed decreases
Solution Approach 1:
The patent uses partial action by only encoding the necessary number of bins required to represent each DQP value within its sign-specific range. Instead of encoding all possible DQP values uniformly, the system encodes only the relevant portion for each sign category, achieving sufficient precision while maintaining high processing speed through reduced bin count
Data Source
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AI summary
An image quantization parameter encoder includes: a prediction unit 11 for generating a predicted quantization parameter from a past reconstructed quantization parameter; a computing unit 12 for generating a delta quantization parameter from a quantization parameter and the predicted quantization parameter; and a quantization parameter encoding unit 13 for binary arithmetic encoding a first bin indicating whether or not the delta quantization parameter is significant, one or more other bins indicating an absolute value of the delta quantization parameter and made non-redundant, and a bin indicating whether the delta quantization parameter is positive or negative, in the case where the delta quantization parameter is significant.