TCQ Step Size Scaling for Transform Coefficient Compression
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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 exploit correlations between coefficients.
Innovation Solution
Implement trellis coded quantization (TCQ) that generates and evaluates different permutations of quantization indices based on a cost function balancing distortion and entropy coding efficiency, allowing for increased compression opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scalar quantization is used to independently process each transform coefficient, then the quantization process is simple and fast, 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 vectors for joint quantization processing. Instead of independently quantizing each coefficient as in scalar quantization, the system groups coefficients (e.g., 2D blocks of 4x4 or 8x8 coefficients) and applies vector quantization that considers correlations between coefficients within each vector, thereby improving entropy coding effectiveness while maintaining manageable computational complexity through block-based processing.
Solution Approach 2:
The patent transitions from scalar (1D) quantization to vector (multi-dimensional) quantization by organizing transform coefficients into vectors with multiple dimensions. This dimensional expansion allows the quantization process to exploit correlations between coefficients that exist in the spatial or frequency domain, enabling more effective compression while the systematic vector processing approach keeps implementation complexity controlled.
2Loss of information
If scalar quantization maps different transform coefficients to different quantization indices, then individual coefficient precision is maintained, but opportunities for increased compression during entropy coding are lost
Solution Approach 1:
By merging multiple transform coefficients into vectors and applying joint quantization, the system captures correlations between coefficients. This allows the quantization process to maintain effective representation of the signal while producing quantization indices that exploit these correlations, thereby improving compression efficiency during entropy coding without sacrificing essential information.
Solution Approach 2:
The patent changes the quantization parameter structure from individual scalar step sizes to vector-based step size configurations. By defining step sizes that operate on vectors rather than scalars, and by allowing different step size configurations for different vector positions or patterns, the system optimizes both precision retention and compression efficiency simultaneously.
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 step size scaling value based on contextual metadata associated with the transform coefficients. The encoder computes a first quantizer step size based on the block step size scaling value. The encoder computes a second quantizer step size based on the block step size scaling value. The encoder performs trellis coded quantization operations on the vector of transform coefficients using the first quantizer step size and the second quantizer step size 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.