Vector Quantization for Frequency-Domain Signals
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Solution Overview
Problem
High-performance vector quantization methods, such as SVQ, can cause spectral holes and musical noise when used in coding and decoding apparatuses for frequency-domain signals due to insufficient bit budget, leading to frequency component loss.
Innovation Solution
A method that calculates a normalization value for a predetermined number of input samples, quantizes it, and uses the quantized value to determine quantization candidates, which are then jointly vector-quantized to prevent spectral holes and reduce musical noise by actively quantizing dominant frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-performance vector quantization methods (such as SVQ) are used to quantize frequency-domain signals, then quantization noise is reduced, but spectral holes occur due to insufficient bit budget, causing frequency component loss
Solution Approach 1:
The patent applies local quality by differentiating the quantization treatment for different frequency components. Dominant frequency components (those with larger magnitudes) are allocated more bits for quantization, while non-dominant components receive fewer bits. This non-uniform quantization strategy ensures that important frequency components are preserved with high precision, preventing spectral holes in critical frequency regions, while accepting coarser quantization for less important components.
Solution Approach 2:
The patent changes the quantization parameter allocation dynamically based on the signal characteristics. By identifying dominant frequency components and adjusting the bit allocation accordingly, the system adapts the quantization precision to match the actual information content of different frequency regions, thereby preventing spectral holes while maintaining overall quantization efficiency.
2Productivity
If vector quantization is applied to frequency-domain signals with limited bit budget, then coding efficiency is improved, but musical noise is caused due to spectral holes from missing frequency components
Solution Approach 1:
The patent eliminates musical noise by applying local quality differentiation in frequency domain quantization. By allocating quantization bits according to the magnitude of each frequency component, dominant components are preserved accurately while non-dominant components are coarsely quantized or set to zero. This prevents the random absence of frequency components that causes musical noise, while still achieving efficient coding through selective quantization.
Solution Approach 2:
The patent uses a codebook-based approach where pre-stored vectors representing typical frequency patterns are used to approximate the input signal. By matching the input frequency components against codebook entries and selecting the best match, the system efficiently reconstructs the signal with reduced bits, preventing spectral holes and musical noise through intelligent pattern matching rather than simple truncation.
Data Source
AI summary
A normalization value calculator 12 calculates a normalization value that is representative of a predetermined number of input samples. A normalization value quantizer 13 quantizes the normalization value to obtain a quantized normalization value and a normalization-value quantization index corresponding to the quantized normalization value. An quantization-candidate calculator 14 subtracts a value corresponding to the quantized normalization value from a value corresponding to the magnitude of each of the samples to obtain a difference value and, when the difference value is positive and the value of each of the samples is positive, sets the difference value as an quantization candidate corresponding to the sample. When the difference value is positive and the value of each of the samples is negative, the quantization-candidate calculator 14 reverses the sign of the difference value and setting the sign-reversed value as an quantization candidate corresponding to the sample. When the difference value is not positive, the quantization-candidate calculator 14 sets 0 as an quantization candidate corresponding to the sample. A vector quantizer 15 jointly vector-quantizes a plurality of quantization candidates corresponding to a plurality of samples to obtain a vector quantization index.