Implementation of vector quantization technique in image compression and speech recognition
Summary :
Table of Contents
- Abstract
- Introduction
- Codebook design
- LBG algorithm
- Mathematical analysis of LBG algorithm
- Splitting codebook design
- Experiment and output
- Vector quantization in speech recognition
- Algorithm
- Pre processing
- Mel frequency wrapping (MFCC)
- Principle of MFCC
- Steps used in MFCC
- Vector quantization
- Initialization
- Vector coding
- Codebook updating
- Quantization error calculation
- Results
- Conclusion
- References
Abstract
In this paper, we have described the application of vector quantization in image and speech processing. Compressing image data by using vector quantization (VQ)[1]-[3] will compare Training Vectors with Codebook. The result is an index of position with minimum distortion. Basically there are three ways of generating the code book: 1)The random method, 2)Pair wise nearest neighbor clustering and 3)Splitting. The implementing Random Codebook will reduce the image quality. This paper presents the Splitting solution to implement the Codebook, which improves the image quality by the average Training Vectors, then splits the average result to Codebook that has minimum distortion. The result from this presentation will give the better quality of the image than using Random Codebook.
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