Name
Title | ||
|---|---|---|
Low-complexity predictive lossy compression of hyperspectral and ultraspectral images |
Abstract | ||
|---|---|---|
Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but its complexity and memory requirements are unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, uniform threshold quantization, and rate-distortion optimization. Its performance is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower. The algorithm is able to limit the scope of errors, and is amenable to parallel implementation, making it suitable for onboard compression at high throughputs. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946524 | ICASSP |
Keywords | Field | DocType |
optimisation,onboard compression,image coding,quantisation (signal),3d transform coding,transform coding,hyperspectral image compression,ultraspectral image compression,computational complexity,rate-distortion optimization,geophysical image processing,uniform-threshold quantization,parallel implementation,rate distortion theory,low-complexity predictive lossy compression,lossy compression,high throughput,prediction algorithms,quantization,hyperspectral imaging,rate distortion optimization,optimization | Computer science,Artificial intelligence,Rate–distortion theory,Context-adaptive binary arithmetic coding,Computer vision,Data compression ratio,Lossy compression,Pattern recognition,Algorithm,Transform coding,Data compression,Image compression,Lossless compression | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4577-0537-3 | 978-1-4577-0537-3 | 6 |
PageRank | References | Authors |
0.59 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Abrardo | 1 | 376 | 47.39 |
| M. Barni | 2 | 3091 | 246.21 |
| Enrico Magli | 3 | 1319 | 114.81 |