Trellis Quantization Indexing for Efficient Entropy Coding
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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 detecting indexes corresponding to inputs from a trellis coded quantization (TCQ) codebook, entropy-encoding these indexes, and classifying quantization levels to select cosets in a predetermined state, allowing for efficient de-quantization by grouping cosets that cannot coexist on branches, thereby optimizing the indexing and decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If trellis quantization coding is used, then encoding complexity is reduced compared to unstructured vector quantization, but entropy encoding efficiency deteriorates due to random path variations
Solution Approach 1:
The patent applies preliminary action by pre-classifying quantization levels and pre-organizing the codebook structure before encoding operations. The codebook is prepared with quantization levels grouped by coset relationships, and the trellis structure is pre-configured with state transitions that map to specific cosets. This preprocessing eliminates the need for complex random path searches during actual encoding, thereby improving entropy encoding efficiency while maintaining the low complexity advantage of trellis quantization.
2Productivity
If cosets are allocated to branches in trellis structure, then quantization levels can be classified for efficient indexing, but the complexity of managing coset allocations increases
Solution Approach 1:
The patent applies segmentation by dividing the quantization levels into distinct coset groups, where each coset represents a segment of the overall quantization space. The codebook is segmented such that quantization levels are organized by their coset membership, and the trellis branches are segmented to correspond to specific cosets. This segmentation enables efficient indexing within each coset segment while reducing the overall management complexity through modular organization.
Solution Approach 2:
The patent applies partial action by focusing coset allocation and classification only on the necessary portions of the quantization space that are actively used in the trellis transitions. Rather than managing all possible quantization levels uniformly, the system selectively applies coset-based organization to the specific branches and states that require it, thereby improving indexing efficiency without unnecessarily increasing management complexity across the entire system.
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.