We investigate the ability to derive meaningful information from decompressed imaging spectrometer data. Hyperspectral images are compressed with near-lossless and lossy coding methods. Linear prediction between the bands is used in both cases. Each band is predicted by a previously transmitted band. The residual is formed by subtracting the prediction from the original data and then is compressed either with a near-lossless bit-plane coder or with the lossy JPEG2000 algorithm. We study the effects of these two types of compression on hyperspectral image processing such as mineral and vegetation content classification using whole- and mixed pixel analysis techniques. The results presented in this paper indicate that an efficient lossy coder outperforms near-lossless method in terms of its impact on final hyperspectral data applications.

For Deaf people, access to the mobile telephone network in the United States is currently limited to text messaging, forcing communication in English as opposed to American Sign Language (ASL), the preferred language. Because ASL is a visual language, mobile video phones have the potential to give Deaf people access to real-time mobile communication in their preferred language. However, even today's best video compression techniques can not yield intelligible ASL at limited cell phone network bandwidths. Motivated by this constraint, we conducted one focus group and two user studies with members of the Deaf Community to determine the intelligibility effects of video compression techniques that exploit the visual nature of sign language. Inspired by eye tracking results that show high resolution foveal vision is maintained around the face, we studied region-of-interest encodings (where the face is encoded at higher quality) as well as reduced frame rates (where fewer, better quality, frames are displayed every second). At all bit rates studied here, participants preferred moderate quality increases in the face region, sacrificing quality in other regions. They also preferred slightly lower frame rates because they yield better quality frames for a fixed bit rate. The limited processing power of cell phones is a serious concern because a real-time video encoder and decoder will be needed. Choosing less complex settings for the encoder can reduce encoding time, but will affect video quality. We studied the intelligibility effects of this tradeoff and found that we can significantly speed up encoding time without severely affecting intelligibility. These results show promise for real-time access to the current low-bandwidth cell phone network through sign-language-specific encoding techniques.

Algorithms for lossless and lossy compression of hyperspectral images are presented. To greatly reduce the bit rate required to code images and to exploit the large amount of inter-band correlation, linear prediction between the bands is used. Each band, except the first one, is predicted by previously transmitted band. Once the prediction is formed, it is subtracted from the original ∗This work appeared in part in the Proceedings of the NASA Earth Science Technology Conference, 2003, and in the Proceedings of the Data Compression Conference, 2004. Research supported by NASA Contract NAS5-00213 and National Science Foundation grant number CCR-0104800. Scott Hauck was supported in part by an NSF CAREER Award and an Alfred P. Sloan Research Fellowship. Contact information: Professor Richard Ladner, University of Washington, Box 352500, Seattle, WA 981952500, (206) 543-9347, ladner@cs.washington.edu. band, and the residual (difference image) is compressed. To find the best prediction algorithm, the impact of various band orderings and measures of prediction quality on the compression ratios is studied. The resulting lossless compression algorithm displays performance that is comparable with other recently published results. To reduce the complexity of the lossy predictive encoder, a bit plane-synchronized closed loop predictor that does not require full decompression of a previous band at the encoder is proposed. The new technique achieves similar compression ratios to that of standard closed loop predictive coding and has a simpler implementation.

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.