A Novel Self-Creating Neural Network for Learning Vector Quantization
โ Scribed by Jung-Hua Wang; Chung-Yun Peng
- Book ID
- 110277389
- Publisher
- Springer US
- Year
- 2000
- Tongue
- English
- Weight
- 264 KB
- Volume
- 11
- Category
- Article
- ISSN
- 1370-4621
No coin nor oath required. For personal study only.
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The basic goal of image compression through vector generates the address of the codevector specified by Q(x); and quantization (VQ) is to reduce the bit rate for transmission or data a decoder, which uses this address to generate the codevector y. storage while maintaining an acceptable fidelity or
The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines th