Training set synthesis for entropy-constrained transform vector quantization

Dorin Comaniciu

Abstract: This paper introduces the concept of training set synthesis for entropy-constrained transform vector quantization (TSS-ECTVQ). The statistics of actual sets of transform vectors are first approximated using histograms. New sets of vectors-called synthesized training sets-are obtained based upon the estimated parameters-called training set parameters. By employing a fast entropy-constrained algorithm, codebooks are populated from the synthesized training sets for each image being coded. Then, entropy-constrained vector quantization is performed. The training set parameters are sent to the decoder, which obtains the same training sets, and generates codebooks identical to the encoder. Experimental results demonstrate that high quality image coding at low bit rates can be obtained with the proposed TSS-ECTVQ method. In particular, the image Lenna was coded at 0.25 bits/pixel with a PSNR of 32.42 dB.