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Technical Program
Paper Detail
| Paper: | TA-L2.6 |
| Session: | Image Coding |
| Time: | Tuesday, October 10, 11:40 - 12:00 |
| Presentation: |
Lecture
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| Title: |
NEAR-LOSSLESS COMPRESSION OF HYPERSPECTRAL IMAGES |
| Authors: |
Agnieszka Miguel; Seattle University | | | | | Jenny Liu; University of Washington | | | | | Dane Barney; University of Washington | | | | | Richard Ladner; University of Washington | | | | | Eve Riskin; University of Washington | | |
| Abstract: |
Algorithms for near-lossless compression of hyperspectral images are presented. They guarantee that the intensity of any pixel in the decompressed image(s) differs from its original value by no more than a user-specified quantity. To reduce the bit rate required to code images while providing significantly more compression than lossless algorithms, linear prediction between the bands is used. Each band is predicted by a previously transmitted band. The prediction is subtracted from the original band, and the residual is compressed with a bit plane coder which uses context-based adaptive binary arithmetic coding. To find the best prediction algorithm, the impact of various band orderings and optimization techniques on the compression ratios is studied. |
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