Vector Quantization Using Basic and Adjustment Codebooks
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Solution Overview
Problem
The complexity of vector quantization in digital signal encoding, particularly in audio and video compression, is exacerbated by high storage and calculation loads due to the need for massive codebook vectors and multi-stage quantization processes, which increase computational burden and reduce encoding efficiency.
Innovation Solution
A method and device for vector quantization that utilize a basic codebook vector and adjustment vectors to form a joint codebook vector, reducing the number of codebooks required and simplifying the quantization process by leveraging the relevance between dimensions, thereby decreasing storage and calculation loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-stage codebook vector quantization is used to ensure encoding quality, then encoding quality is improved, but storage load and calculation load increase exponentially
Solution Approach 1:
The codebook is segmented into a basic codebook and multiple adjustment vector collections. Instead of storing all codebook vectors, only the basic codebook vectors and adjustment vectors are stored. The actual codebook vectors are generated on-demand by combining basic codebook vectors with adjustment vectors, significantly reducing storage load while maintaining encoding quality.
Solution Approach 2:
The basic codebook vectors and adjustment vectors are prepared in advance and stored. During encoding, the actual codebook vectors are generated by combining these pre-prepared components based on the input vector, avoiding the need to store and process all possible codebook vectors beforehand.
2Productivity
If the vector dimension is increased to reduce encoding rate, then encoding rate is reduced, but complexity of vector quantization increases
Solution Approach 1:
The high-dimensional vector quantization problem is segmented into selecting from a basic codebook and then selecting adjustment vectors. This segmentation reduces the computational complexity by breaking down the search space into manageable components that can be processed sequentially rather than exhaustively searching all codebook vectors.
Solution Approach 2:
The approach changes the parameter representation from storing complete codebook vectors to storing basic codebook vectors and adjustment vectors. This parameter change reduces the dimensionality of the stored data while maintaining the ability to reconstruct high-dimensional codebook vectors when needed.
3Measurement precision
If massive codebook vectors are stored to improve quantization accuracy, then quantization accuracy is improved, but storage load increases
Solution Approach 1:
The codebook is segmented into a basic codebook and adjustment vector collections. Only the essential basic codebook vectors and adjustment vectors are stored, rather than all possible codebook vectors. The actual codebook vectors are generated on-demand by combining these stored components, significantly reducing storage load while maintaining quantization accuracy.
Solution Approach 2:
Instead of storing all codebook vectors directly, the system stores a compact representation (basic codebook vectors and adjustment vectors) that can be used to generate or copy the actual codebook vectors when needed. This copying mechanism reduces storage requirements while maintaining access to the full codebook functionality.
Data Source
AI summary
A method for quantizing vector. The method includes: performing a quantization process on a vector to be quantized by use of N basic codebook vectors and the adjustment vectors of each of the basic codebook vectors, generating a basic codebook vector and an adjustment vector used for quantizing the vectors to be quantized, N being a positive integer larger than or equal to 1. According to the present invention, based on the method a device for quantizing vector is disclosed. According to embodiments of the present invention, the quantization of an input vector is done by introducing the modification vectors for the base codebook vectors, therefore the memory amount of the base codebook vectors is reduced effectively, and the calculation amount is merely the calculation amount required for going through N codebooks. Therefore, the complexity of the vector quantization could be decreased effectively.