Model-based vector quantization with application to remotely sensed image data

Abstract

Model-based vector quantization (MVQ) is introduced here as a variant of vector quantization (VQ). MVQ has the asymmetrical computational properties of conventional VQ, but does not require the use of pregenerated codebooks. This is a great advantage, since codebook generation is usually a computationally intensive process, and maintenance of codebooks for coding and decoding can pose difficulties. MVQ uses a simple mathematical model for mean removed errors combined with a human visual system model to generate parameterized codebooks. The error model parameter (lambda) is included with the compressed image as side information from which the same codebook is regenerated for decoding. As far as the user is concerned, MVQ is a codebookless VQ variant. After a brief introduction, the problems associated with codebook generation and maintenance are discussed. We then give a description of the MVQ algorithm, followed by an evaluation of the performance of MVQ on remotely sensed image data sets from NASA sources. The results obtained with MVQ are compared with other VQ techniques and JPEG/DCT. Finally, we demonstrate the performance of MVQ as a part of a progressive compression system suitable for use in an image archival and distribution installation.