Name
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. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/83.736678 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
codebookless vq variant,remote sensing,asymmetrical computational properties,image distribution,image coding,codebook generation,model-based vector quantization,parameterized codebooks,side information,codebook maintenance,image data set,error model parameter,errors,image archival,vq technique,vector quantisation,compressed image,geophysical signal processing,mvq algorithm,human visual system model,conventional vq,mvq,remotely sensed image data,decoding,human visual system,indexing terms,data compression,mathematical model,transform coding,vector quantization | Computer vision,Pattern recognition,Human visual system model,Discrete cosine transform,Image processing,JPEG,Vector quantization,Artificial intelligence,Decoding methods,Data compression,Mathematics,Codebook | Journal |
Volume | Issue | ISSN |
8 | 1 | 1057-7149 |
Citations | PageRank | References |
1 | 0.38 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M Manohar | 1 | 1 | 0.38 |
| J. C. Tilton | 2 | 29 | 4.07 |