Name
Abstract | ||
|---|---|---|
We present a new low-complexity algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. We introduce a simple heuristic to estimate the performance of the linear predictor from a pixel spatial context and a context modeling mechanism with one-band look-ahead capability, which improves the overall compression with marginal usage of additional memory. The pr... |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/LSP.2004.840907 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Image coding,Hyperspectral imaging,Context modeling,Space vehicles,Hardware,Energy consumption,NASA,Infrared imaging,Infrared spectra,Spectroscopy | Imaging spectrometer,Pattern recognition,Computer science,Context model,Linear prediction,Hyperspectral imaging,Artificial intelligence,Pixel,Data compression,Linear predictive coding,Lossless compression | Journal |
Volume | Issue | ISSN |
12 | 2 | 1070-9908 |
Citations | PageRank | References |
46 | 2.29 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. RIZZO | 1 | 79 | 8.79 |
| B. Carpentieri | 2 | 136 | 12.01 |
| Giovanni Motta | 3 | 88 | 8.98 |
| James A. Storer | 4 | 931 | 156.06 |