Centroid-Sorted Vector Quantizer for Adaptive Codebook Search
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Solution Overview
Problem
Vector quantization systems face challenges in balancing computational complexity and accuracy, especially when quantizing multiple vectors simultaneously, as existing optimization techniques struggle to maintain quality and limit complexity constraints.
Innovation Solution
A codebook is divided into classes and sorted based on centroids, allowing for a dynamic adaptation of the search space by prioritizing the most probable candidate codevectors, reducing computational complexity while maintaining high accuracy.
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, where each class contains a subset of codevectors. This segmentation allows the search to be performed on smaller subsets rather than the entire codebook, reducing computational complexity while maintaining the ability to achieve high accuracy when needed.
Solution Approach 2:
The codebook classes are pre-sorted based on distortion measures and statistical properties before the quantization process. This preliminary organization enables the algorithm to quickly identify and search only the most relevant classes containing probable candidate codevectors, avoiding exhaustive search of the entire codebook.
2Measurement precision
If the codebook size K is increased to improve quantization accuracy, then the reconstruction quality improves, but the memory storage requirements increase
Solution Approach 1:
By dividing the codebook into classes and storing them in an organized hierarchical structure, the patent enables more efficient memory utilization. The segmentation allows the system to access only relevant portions of the codebook, reducing the effective memory footprint during operation.
3Device complexity
If off-line optimization techniques are used to reduce computational complexity, then the complexity is reduced, but the ability to maintain quality constraints when quantizing multiple vectors simultaneously deteriorates
Solution Approach 1:
The patent introduces a dynamic approach where the search process adapts based on the number of input vectors to be quantized. When multiple vectors are to be quantized, the algorithm dynamically adjusts which codebook classes to search and in what order, based on statistical analysis of the input vectors. This dynamic adaptation maintains quality constraints while optimizing complexity for the specific situation.
Solution Approach 2:
The algorithm changes search parameters (such as the set of active codebook classes and search depth) based on the number of input vectors and complexity constraints. This parameter adaptation allows the system to maintain quality requirements under varying operational conditions while optimizing computational efficiency.
Data Source
AI summary
Vector Quantizer and method therein for vector quantization, e.g. in a transform audio codec. The method comprises comparing an input target vector with four centroids C0, C1, C0,flip and C1,flip, wherein centroid C0,flip is a flipped version of centroid C0 and centroid C1,flip is a flipped version of centroid C1, each centroid representing a respective class of codevectors. A starting point for a search related to the input target vector in the codebook is determined, based on the comparison. A search is performed in the codebook, starting at the determined starting point, and a codevector is identified to represent the input target vector. A number of input target vectors per block or time segment is variable. A search space is dynamically adjusted to the number of input target vectors. The codevectors are sorted according to a distortion measure reflecting the distance between each codevector and the centroids C0 and C1.