Name
Abstract | ||
|---|---|---|
High dimensional source vectors, such as occur in hyperspectral imagery, arepartitioned into a number of subvectors of (possibly) different length and then eachsubvector is vector quantized (VQ) individually with an appropriate codebook. A locallyadaptive partitioning algorithm is introduced that performs comparably in this applicationto a more expensive globally optimal one that employs dynamic programming. The VQindices are entropy coded and used to condition the lossless or near-lossless coding of theresidual error. Motivated by the need of maintaining uniform quality across all vectorcomponents, a Percentage Maximum Absolute Error distortion measure is employed.Experiments on the lossless and near-lossless compression of NASA AVIRIS images arepresented. A key advantage of our approach is the use of independent small VQcodebooks that allow fast encoding and decoding. |
Year | Venue | Keywords |
|---|---|---|
2003 | Int. Conf. on E-Business and Telecommunication Networks | nasa aviris image,independent small vqcodebooks,different length,near-lossless compression,percentage maximum absolute error,hyperspectral imagery,high dimensional source vector,dynamic programming,distortion measure,appropriate codebook,entropy,decoding,entropy coding,hyperspectral sensors,global optimization,lossless compression,remote sensing,hyperspectral imaging,encoding,codebook,vector quantization |
DocType | ISSN | ISBN |
Conference | 1068-0314 | 0-7695-1896-6 |
Citations | PageRank | References |
17 | 1.82 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Motta | 1 | 88 | 8.98 |
| Francesco Rizzo | 2 | 86 | 11.21 |
| James A. Storer | 3 | 931 | 156.06 |