Trellis Quantization Indexing for Efficient De-Quantization
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Solution Overview
Problem
Trellis quantization coding methods face inefficiencies in entropy encoding due to random path variations, leading to suboptimal performance in encoding and decoding processes.
Innovation Solution
The method involves using a trellis coded quantization (TCQ) codebook with cosets allocated to quantization levels, where indexes are entropy-encoded and decoded to select specific branches in a trellis structure, allowing for efficient de-quantization by grouping cosets that cannot coexist on the same branch, and employing new indexing methods to classify and index quantization levels effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If trellis quantization coding is used to reduce complexity compared to unstructured vector quantization, then device complexity is reduced, but entropy encoding efficiency deteriorates due to random path variations
Solution Approach 1:
The patent segments the trellis codebook into multiple sub-codebooks or partitions, allowing the entropy encoder to process only relevant segments based on the current state and input. This reduces the search space and improves encoding efficiency while maintaining the overall trellis structure's complexity advantages.
Solution Approach 2:
The patent performs preliminary organization of the trellis codebook structure, pre-calculating and storing path statistics or probability information. This preliminary action allows the entropy encoder to make faster decisions during actual encoding by referencing pre-processed data rather than computing from scratch, thus improving efficiency without increasing runtime complexity.
2Productivity
If cosets are allocated to quantization levels in TCQ codebook, then de-quantization efficiency is improved by enabling branch selection using only indexes, but the complexity of codebook structure and index allocation increases
Solution Approach 1:
The patent divides the codebook into multiple cosets, each associated with specific quantization levels. This segmentation allows the de-quantizer to efficiently determine the correct branch by checking only the relevant coset index rather than searching the entire codebook, improving de-quantization efficiency while organizing complexity into manageable segments.
Solution Approach 2:
The patent performs preliminary allocation of cosets to quantization levels during codebook construction, establishing a fixed mapping relationship. This preliminary organization enables the de-quantizer to directly translate indexes into branch selections without complex runtime computations, trading off initial setup complexity for runtime efficiency.
3Productivity
If new indexing methods are used to classify quantization levels, then encoding efficiency is improved by stabilizing the encoding process, but the complexity of index allocation and codebook management increases
Solution Approach 1:
The patent introduces new indexing parameters or transforms the existing index space to create a more stable encoding process. By changing the parameter representation of quantization levels, the encoder achieves more consistent performance and reduced variability, while the index allocation complexity is managed through systematic transformation rules rather than ad-hoc methods.
Data Source
AI summary
Provided are a method and apparatus for quantization encoding and de-quantization decoding using a trellis. Unlike a trellis coded quantization (TCQ) index, by classifying quantization levels to which cosets are allocated and allocating indexes to the quantization levels so that a coset corresponding to a specific branch in a predetermined state in the trellis can be selected with only indexes without encoding or decoding information on paths, quantization encoding and de-quantization decoding are performed by using a new index.