Conditional-entropy-constrained trellis-searched vector quantization for image compression

Abstract This paper proposes conditional-entropy-constrained trellis-searched vector quantization for image compression. We introduce (i) the conditional-entropy index encoder to exploit interblock correlation and (ii) the trellis-searched encoding to get a long-term optimum. Simulations show that the proposed VQ provides higher PSNR by 3–5 dB, when compared to ECVQ.

[1]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[2]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[3]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[4]  Philip A. Chou,et al.  Variable rate vector quantization for speech, image, and video compression , 1993, IEEE Trans. Commun..

[5]  Philip A. Chou,et al.  Entropy-constrained vector quantization , 1989, IEEE Trans. Acoust. Speech Signal Process..

[6]  Robert M. Gray,et al.  Simulation of Vector Trellis Encoding Systems , 1986, IEEE Trans. Commun..