Class-Sorted Vector Quantization Under Complexity Constraints
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Solution Overview
Problem
Vector quantization (VQ) systems face challenges in balancing computational complexity and accuracy, especially when quantizing multiple vectors simultaneously, as existing optimization techniques struggle to maintain quality and complexity constraints, particularly in audio transmission systems where computational complexity must be limited.
Innovation Solution
The proposed solution involves a codebook divided into classes and sorted based on a distortion measure, allowing for dynamic adaptation of the search space based on the number of input vectors and a maximum complexity constraint, enabling efficient search for the best match codevector while maintaining high accuracy and controlling computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the codebook size K is increased to improve quantization accuracy, then the reconstruction quality improves, but the computational complexity increases
Solution Approach 1:
The codebook is divided into multiple classes or subsets, allowing the search to be performed in a segmented manner. Instead of searching the entire codebook of size K, the algorithm partitions the codebook into smaller groups and performs targeted searches within relevant subsets, reducing the effective search space while maintaining accuracy.
Solution Approach 2:
The codebook is pre-sorted or pre-organized into classes based on certain criteria before the quantization process. This preliminary organization allows the encoder to quickly identify and search only the relevant codebook classes for a given input vector, avoiding unnecessary comparisons with unrelated codevectors and thus reducing computational complexity.
2Measurement precision
If the codebook size K is increased to maintain high accuracy when quantizing multiple vectors, then the quantization quality is maintained, but the computational complexity exceeds the maximum limit L MAX
Solution Approach 1:
The search space size is made dynamic rather than fixed. The algorithm adapts the number of codebook vectors to search based on the number of input vectors N and the complexity constraint L MAX. When N is large, the search space is reduced to maintain complexity limits; when N is small, the full codebook can be searched to maintain accuracy.
Solution Approach 2:
The effective codebook size parameter is changed dynamically based on operating conditions. Instead of always using the full codebook size K, the algorithm adjusts the search space parameter according to the number of input vectors and complexity constraints, allowing optimal performance across different operating scenarios.
Data Source
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AI summary
Vector Quantizer and method therein for efficient vector quantization, e.g. in a transform audio codec. The method comprises comparing an input target vector s with a plurality of centroids, each centroid representing a respective class of codevectors in a codebook. Further, a starting point for a search related to the input target vector in the codebook is determined, based on the result of the comparison.The codevectors in the codebook are sorted according to a distortion measure reflecting the distance between each codevector and the centroids of the classes. The Vector Quantizer and method enables that the class of codevectors comprising the most probable candidate codevectors in regard of the input vector. s may be searched first.