We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.
Bibliographical noteFunding Information:
Manuscript received July 16, 1996; revised January 6, 1998. This work was supported by Samsung Electronics Company. Preliminary results of this work were published in Proc. ICIP’95, vol. 3, pp. 268–271. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Amy R. Reibman.
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design