Vector Quantization Coefficient Indexing to Reduce Musical Noise
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Solution Overview
Problem
Existing vector quantization methods, such as SVQ and AVQ, suffer from musical noise and block noise due to low amplitude quantization precision, which affects both frequency-domain and time-domain signals.
Innovation Solution
An encoding and decoding method that outputs index information indicating a group of coefficients minimizing the error between sample values and their quantized counterparts, allowing for reduced quantization error by adjusting decoded values with predetermined coefficients, thereby minimizing musical noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spherical vector quantization (SVQ) method is used to quantize samples in units of sub-bands with dynamic bit assignment, then quantization precision for sparse signals is improved, but musical noise occurs when input signals have energy in many frequencies
Solution Approach 1:
The patent divides the frequency spectrum into multiple sub-bands and processes each sub-band separately with dynamic bit assignment. This segmentation allows preferential quantization of dominant frequency components while using fewer bits for less important components, reducing overall quantization noise and preventing musical noise artifacts.
Solution Approach 2:
The patent applies different quantization precision to different sub-bands based on their perceptual importance and energy distribution. Sub-bands containing significant signal energy receive higher quantization precision, while sub-bands with minimal energy use lower precision, optimizing the balance between quality and bit rate while avoiding musical noise.
2Loss of information
If algebraic vector quantization (AVQ) method is used to restore more frequency components, then completeness of decoded signals is improved, but amplitude quantization precision deteriorates
Solution Approach 1:
The patent dynamically adjusts the number of bits allocated to each sub-band based on the signal characteristics and perceptual importance. This dynamic bit allocation allows the system to adaptively optimize between restoring frequency components and maintaining amplitude precision, depending on the specific signal conditions.
Solution Approach 2:
The patent changes the quantization parameters (number of bits per sub-band) based on the energy distribution and perceptual importance of different frequency components. By adjusting these parameters dynamically, the system can restore more frequency components when needed while maintaining adequate amplitude precision in critical sub-bands.
Data Source
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AI summary
In encoding, index information indicating a group of coefficients that minimizes the sum of the error between the value of each sample and the value obtained by multiplying the quantized value of the sample by a coefficient corresponding to the position of the sample, for all sample positions, among a plurality of groups of predetermined coefficients corresponding to the positions of the samples, is output. In decoding, a plurality of values corresponding to an input vector quantization index are obtained as decoded values corresponding to a plurality of sample positions; and, with the use of a group of predetermined coefficients corresponding to the plurality of sample positions and indicated by input index information, the values obtained by multiplying the decoded values and the coefficients, corresponding to the sample positions are output.