Video Trellis Quantization for Correlated Coefficient Coding
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Solution Overview
Problem
Conventional scalar quantization methods for video encoding fail to account for correlations between transform coefficients, leading to reduced effectiveness in subsequent entropy coding and overall encoding efficiency.
Innovation Solution
Implement trellis coded quantization (TCQ) to generate sequences of quantization indices that consider correlations between transform coefficients, using a cost function to evaluate different permutations for optimal compression during entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If scalar quantization is used to simplify the quantization process, then device complexity is reduced, but encoding efficiency deteriorates due to inability to exploit correlations between transform coefficients
Solution Approach 1:
The quantization process is segmented into multiple stages: scalar quantization is applied to each transform coefficient individually first, then a secondary optimization stage processes sequences of quantization indices to exploit correlations. This segmentation allows the system to benefit from both the simplicity of scalar quantization and the efficiency of correlation-based optimization.
Solution Approach 2:
Scalar quantization is performed as a preliminary action on each transform coefficient before the more complex correlation-based optimization is applied. This preliminary quantization reduces the data dimensionality and creates a foundation that the subsequent optimization stage can build upon, improving overall encoding efficiency without requiring complete redesign of the quantization process.
2Speed
If scalar quantization is applied independently to each transform coefficient, then processing speed is improved, but compression effectiveness deteriorates due to loss of correlation information
Solution Approach 1:
The processing is segmented into two phases: a fast scalar quantization phase that processes each coefficient independently at high speed, followed by a correlation exploitation phase that processes sequences of quantization indices. This segmentation preserves processing speed while recovering correlation information that would otherwise be lost.
Solution Approach 2:
The sequence of quantization indices generated by scalar quantization serves as an intermediary that carries both the quantized values and implicit correlation information. The patent applies additional processing to this intermediary representation to explicitly exploit correlations, thereby recovering information that survived the initial independent quantization step.
3Manufacturing precision
If more bits are used to represent transform coefficients, then manufacturing precision is improved, but data compression ratio deteriorates
Solution Approach 1:
The patent dynamically adjusts the number of bits used to represent quantization indices based on the local characteristics of transform coefficient sequences. In regions with high correlation, fewer bits are needed due to the exploitation of redundancy, while in regions with low correlation, more bits are allocated to preserve precision. This dynamic allocation optimizes the trade-off between precision and compression ratio.
Solution Approach 2:
The patent changes the representation parameters of quantization indices by applying different coding strategies based on correlation patterns. When correlations are detected, the system changes from representing each index independently to using differential coding or context-based coding, effectively reducing the bits required per coefficient while maintaining reconstruction precision.
Data Source
AI summary
In various embodiments, for each state in a stage of a trellis data structure, an encoder computes a cumulative cost function value for each of reconstructing a transform coefficient associated with a block of source video data at a zero quantization index, coding the transform coefficient with a selected sub-quantizer at a closest non-zero quantization index with even parity, and coding the transform coefficient with the selected sub-quantizer at a closest non-zero quantization index with odd parity. The encoder modifies the trellis data structure based on the cumulative cost function values. The encoder generates a vector of quantization indices that corresponds to a path that passes through all stages of the trellis data structure and has a lowest overall cumulative cost function value. The encoder performs entropy coding operations on the vector of quantization indices to generate an encoded version of the block of source video data.