Fast image vector quantization using a modified competitive learning neural network approach
✍ Scribed by Robert Li; Earnest Sherrod; Jung Kim; Gao Pan
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 259 KB
- Volume
- 8
- Category
- Article
- ISSN
- 0899-9457
No coin nor oath required. For personal study only.
✦ Synopsis
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 image quality. The
The signal-noise-ratio (SNR) is usually used to measure the fiadvantage of VQ image compression is its fast decompression by delity or quality of recovered image [4,15]. One would like to table lookup technique. However, the codebook supplied in advance maximize the SNR value given by may not handle the changing image statistics very well. The need for online codebook generation became apparent. The competitive learning neural network design has been used for vector quantization.
SNR(db)
However, its training time can be very long, and the number of output nodes is somewhat arbitrarily decided before the training starts. Our modified approach presents a fast codebook generation procedure where n is the number of input vectors and y j is the output vector by searching for an optimal number of output nodes evolutively. The results on two medical images show that this new approach reduces that is closest to x j in the codebook. By using SNR as a quality the training time considerably and still maintains good quality for criterion, one can compare the performance of different coding recovered images.