Name
Abstract | ||
|---|---|---|
Lossy compression of AVIRIS hyperspectral images is considered. An automatic approach to selection of compression parameters depending on noise characteristics in component images is proposed. Several ways of performing lossy compression are discussed and compared. It is shown that in order to minimize distortions and provide a sufficient compression ratio it is reasonable to group the channels according to the evaluated noise variances in subband images and depending upon the sensor that produces sets of subband images. It is shown that for real life images the attained compression ratios can be of the order 8... 25. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4422833 | IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET |
Keywords | Field | DocType |
lossy compression, subband groupping, blind evaluation of noise variance | Compression (physics),Computer vision,Texture compression,Lossy compression,Computer science,Remote sensing,Hyperspectral imaging,Compression ratio,Artificial intelligence,Data compression,Fractal transform,Image compression | Conference |
ISSN | Citations | PageRank |
2153-6996 | 16 | 0.99 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolay N. Ponomarenko | 1 | 435 | 28.83 |
| Vladimir V. Lukin | 2 | 588 | 57.82 |
| Mikhail Zriakhov | 3 | 30 | 2.85 |
| Arto Kaarna | 4 | 174 | 27.50 |
| Jaakko Astola | 5 | 1515 | 230.41 |