In this paper some improvements in the vector quantization technique are presented. The development of high performance coding techniques for color images is of major importance for the high compression coding framework, previously realized at DIBE. First Linde-Buzo-Gray (LBG) algorithm was improved by introducing a non-supervised neural classifier to choose the starting vector codebook; this strategy allows one to reach better solutions than a random initial choice. Then a âsplit & mergeâ algorithm for codebook vectors was tested in order to obtain a more uniform distribution of the samples of the training sequence among the alphabet letters. Concerning the codifing process, a predictor was designed which allows a considerable reduction in the redundancy remaining after vector quantization, by exploiting the residual âinter-blockâ spatial correlation. During the encoding/de-coding process, the next block can be predicted on the basis of the neighboring blocks, using four spatial-transition probability matrices. The system performance, in terms of SNR, ranges from 28 to 31 dB for a bit rate of about 0.15 bpp; it should be pointed out that the introduction of the predictor produces a sharp reduction of the bit-rate, without loss of information. Â© 1989 SPIE.
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|Titolo:||A framework for high-compression coding of color images|
|Data di pubblicazione:||1989|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|