Rate-Distortion Multiplier Scaling for Trellis Quantization
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Solution Overview
Problem
Conventional scalar quantization methods independently process transform coefficients, reducing the effectiveness of subsequent entropy coding and overall encoding efficiency due to failure to account for correlations between coefficients, leading to missed opportunities for increased compression during entropy coding.
Innovation Solution
Implement trellis coded quantization (TCQ) that generates and evaluates different permutations of quantization indices based on a cost function representing a tradeoff between distortion and entropy coding efficiency, allowing for increased compression opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If scalar quantization is applied independently to each transform coefficient, then the quantization process is simple and computationally efficient, but the effectiveness of subsequent entropy coding is substantially reduced and overall encoding efficiency decreases
Solution Approach 1:
The patent merges multiple transform coefficients into groups and applies joint quantization to exploit correlations between coefficients. Instead of processing each coefficient independently, the system groups coefficients (e.g., in 2x2 blocks) and performs coordinated quantization decisions that consider inter-coefficient relationships, thereby improving entropy coding effectiveness while maintaining computational feasibility.
Solution Approach 2:
The patent transitions from scalar (1D) independent coefficient processing to vector-based joint quantization that operates in higher dimensions by considering groups of coefficients together. This dimensional expansion allows the system to capture correlations that exist in the coefficient space, improving compression efficiency without exponentially increasing complexity.
2Measurement precision
If scalar quantization maps different transform coefficients to different quantization indices, then each coefficient is individually processed, but opportunities for increased compression during entropy coding are lost
Solution Approach 1:
The patent combines multiple transform coefficients into groups for joint quantization, allowing the system to recognize when different coefficients should map to the same quantization index based on their correlations. This merging approach preserves individual coefficient accuracy while creating repeated quantization indices that entropy coding can efficiently compress.
Solution Approach 2:
The patent changes the quantization parameter assignment strategy by using joint quantization decisions that consider multiple coefficients simultaneously. Instead of fixed independent mapping, the system dynamically determines quantization indices based on group-level optimization, creating more repeated values that reduce entropy coding bitrates.
3Productivity
If trellis coded quantization generates and evaluates different permutations of quantization indices, then compression opportunities are increased, but the computational complexity increases
Solution Approach 1:
The patent segments the quantization process into manageable trellis stages, where each stage processes a subset of coefficients. By dividing the overall optimization problem into smaller sequential decisions represented as trellis states, the system can evaluate multiple permutations efficiently using dynamic programming principles, avoiding the need to enumerate all possible combinations.
Solution Approach 2:
The patent performs preliminary organization of transform coefficients into groups and pre-establishes trellis structures before actual quantization. This preliminary action prepares the data in an optimal format for trellis-coded processing, allowing the system to efficiently evaluate permutations during quantization without excessive computational overhead.
Data Source
AI summary
In various embodiments, an encoder generates a vector of transform coefficients of prediction residues that are associated with a block of source video data. The encoder computes a block multiplier scaling value based on contextual metadata associated with the transform coefficients. The encoder computes a first multiplier based on the block multiplier scaling value. The encoder performs trellis coded quantization operations on the vector of transform coefficients using the first multiplier to generate a vector of quantization indices. The encoder performs entropy coding operations on the vector of quantization indices to generate an encoded version of the block of source video data.