The Standard for the Exchange of Earthquake Data (SEED) is a commonly used file format for recording seismographic data. The data description language (DDL) is part of the specification of the SEED format; it is used to describe the way in which the data has been encoded in the SEED file. All SEED files contain a piece of DDL code that describes how to read the data contained in the file. In this project, we explore the best possible way of compressing seismographic data losslessly under the constraint that it must be describable in DDL. Since DDL is not a Turing-complete language, it is impossible to implement many standard compression algorithms with it. However, we show it is possible to implement a modified Tunstall code under DDL that produces files that are on average 14% smaller than the traditionally used Steim2 compression technique. We also compare our results with a standard linear predictive coding scheme (which is not implementable in DDL) and show that our technique is on average 18% worse on the same set of files.

Hyperspectral imaging is of interest in a large number of remote sensing applications, such as geology and pollution monitoring, in order to detect and analyze surface and atmospheric composition. The processing of these images, called spectral analysis, allows for the identification of the specific mineralogical and agricultural elements which compose an image. We seek to understand how loss due to compression can affect the spectral analysis results, and then modify the compression algorithms to improve spectral analysis performance. More specifically, we suggest modifications to the 3D-SPIHT algorithm for improving the classification accuracy of hyperspectral images for two classification techniques: spectral angle mapper (SAM) and matched filtering (MF). Results of our modification show an improvement in the error rate as reported by the classification techniques, indicating an increase in the ability to analyze hyperspectral images which have been compressed.

For binary two-state Markov sources, we compute the bit-rate for context-independent Golomb coding, sequential coding, and interleaved coding. We also relate these coding methods to image compression work. Sequential coding is a context-dependent method that sequentially codes the source, choosing the order of the elementary Golomb code based on the last bit seen. Interleaved coding codes the even-numbered bits before the odd-numbered bits using elementary Golomb codes of several different orders. Of these methods, we show that no one method is best on all Markov sources.

Summary form only given. Elementary Golomb codes have been widely used for compressing correlated binary sources. We study the theoretical bit-rate performance of two different Golomb coding methods on binary Markov sources: the sequential coding method, and the interleaved coding method. Although the theoretical bit-rate performance for these codes on on i.i.d. sources is known, to the best of our knowledge, theoretical performance results for elementary Golomb codes on correlated Markov sources have not been published.

This paper presents Group Testing for Wavelets (GTW), a novel embedded-wavelet-based image compression algorithm based on the concept of group testing. We explain how group testing is a generalization of the zerotree coding technique for wavelet-transformed images. We also show that Golomb coding is equivalent to Hwang's group testing algorithm. GTW is similar to SPIHT but replaces SPIHT's significance pass with a new group testing based method. Although no arithmetic coding is implemented, GTW performs competitively with SPIHT's arithmetic coding variant in terms of rate-distortion performance.

This paper introduces group testing for wavelet packets (GTWP), a novel embedded image compression algorithm based on wavelet packets and group testing. This algorithm extends the group testing for wavelets (GTW) algorithm to handle wavelet packets. Like its predecessor, GTWP obtains good compression performance without the use of arithmetic coding. It also shows that the group testing methodology is very flexible and can be applied in many different circumstances.

This paper presents the group testing for wavelets algorithm (GTM), which is a novel embedded wavelet-based image compression technique based on the concept of group testing. We explain how group testing is a generalization of the zerotree coding technique for wavelet-transformed images. We also show that Golomb coding is equivalent to Hwang's (Du and Hwang, 1993) group testing algorithm. GTW is similar to SPIHT (Said and Pearlman, 1996) but replaces SPIHT's sorting pass with a new group testing based method. Although no arithmetic coding is implemented, GTW performs competitively with SPIHT's arithmetic coding variant in terms of rate-distortion performance.