Vector Quantization Coding With Corrected Signal Normalization
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Solution Overview
Problem
Conventional coding methods fail to guarantee a small error and improved signal-to-noise ratio (SNR) in decoding digital signals, as the normalization value is solely based on the input signal, leading to potential errors in the output signal.
Innovation Solution
A coding method that normalizes input signals, generates a correction coefficient to minimize the distance between the input and output signal sequences, and quantizes this coefficient to produce a code including signal and normalization information indices, ensuring improved SNR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the normalization value is obtained solely from the input signal, then the coding process is simple, but the output signal error increases and SNR deteriorates
Solution Approach 1:
The patent introduces a feedback mechanism where the normalization value is corrected using information from both the input signal and the reconstructed output signal. The correction coefficient is calculated by comparing the normalized input signal with the reconstructed signal, and this correction is fed back to adjust the normalization value, thereby reducing output error and improving SNR
Solution Approach 2:
The patent introduces a correction coefficient as an intermediary element that mediates between the input signal and the normalization value. This correction coefficient is calculated based on the relationship between the normalized input signal and the reconstructed output signal, and it serves to adjust the normalization value to minimize coding error
2Reliability
If the normalization value is corrected using a correction coefficient, then the SNR improves, but the device complexity increases
Solution Approach 1:
The patent segments the normalization value correction process into distinct functional components: a correction coefficient calculation unit that computes the correction based on input and reconstructed signals, and a normalization value correction unit that applies the correction. This segmentation allows for modular implementation and optimization of each component
Solution Approach 2:
The patent changes the parameter being optimized from merely the input signal energy to a corrected normalization value that incorporates feedback from the reconstructed signal. This parameter change enables the system to adapt the normalization value dynamically to minimize the difference between input and output signals, thereby improving SNR
Data Source
AI summary
A coding method with a small error is provided. In the coding method of the present invention, a normalization value obtained from an input signal is corrected for an error calculated from an input and output in vector quantization and is then quantized. The coding method includes a normalization stage of normalizing the input signal in accordance with the normalization value of the input signal, calculated in each frame; a dividing stage of dividing the normalized frame into divided input signal sequences in accordance with a predetermined rule; a vector quantization stage of applying vector quantization to the divided input signal sequences to generate a vector quantization index; and a normalization value correction stage of correcting the normalization value of the input signal for the error obtained from the input and output in the vector quantization stage.