Edwin S. Hong

Characterizing history independent data structures

We consider history independent data structures as proposed for study by Naor and Teague. In a history independent data structure, nothing can be learned from the memory representation of the data structure except for what is available from the abstract data structure. We show that for the most part, strong history independent data structures have canonical representations. We provide a natural alternative definition of strong history independence that is less restrictive than Naor and Teague and characterize how it restricts allowable representations. We also give a general formula for creating dynamically resizing history independent data structures and give a related impossibility result.

Group testing for image compression

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 Hwangs (Du and Hwang, 1993) group testing algorithm. GTW is similar to SPIHT (Said and Pearlman, 1996) but replaces SPIHTs sorting pass with a new group testing based method. Although no arithmetic coding is implemented, GTW performs competitively with SPIHTs arithmetic coding variant in terms of rate-distortion performance.

Characterizing History Independent Data Structures

We consider history independent data structures as proposed for study by Teague and Naor [3]. In a history independent data structure, nothing can be learned from the representation of the data structure except for what is available from the abstract data structure. We show that for the most part, strong history independent data structures have canonical representations. We also provide a natural less restrictive definition of strong history independence and characterize how it restricts allowable representations. We also give a general formula for creating dynamically resizing history independent data structures and give a related impossibility result.

Group testing for image compression using alternative transforms

This paper extends the group testing for wavelets (IEEE Trans. Image Process. 11 (2002) 901) algorithm to code coefficients from the wavelet packet transform, the discrete cosine transform, and various lapped transforms. Group testing offers a noticeable improvement over zerotree coding techniques on these transforms; is inherently flexible; and can be adapted to different transforms with relative ease. The new algorithms are competitive with many recent state-of-the-art image coders that use the same transforms.

Group testing for wavelet packet image compression

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.

Group testing for block transform image compression

This paper introduces a new image coder based on several different block transforms and on group testing. It extends the Group Testing for Wavelets algorithm by replacing the discrete wavelet transform with the discrete cosine transform, and orthogonal and biorthogonal lapped transforms. In particular, we compare the performance of group testing on different block transforms, and show that on the standard Barbara image, the rate-distortion performance of the group testing on lapped biorthogonal transforms is significantly better than previous zerotree techniques based on the wavelet transform.

Hyperspectral Image Compression with Optimization for Spectral Analysis

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.

Performance modeling of vote-based object recognition

The focus of this paper is predicting the bounds on performance of a vote-based object recognition system, when the test data features are distorted by uncertainty in both feature locations and magnitudes, by occlusion and by clutter. An improved method is presented to calculate lower and upper bound predictions of the probability that objects with various levels of distorted features will be recognized correctly. The prediction method takes model similarity into account, so that when models of objects are more similar to each other, then the probability of correct recognition is lower. The effectiveness of the prediction method is validated in a synthetic aperture radar (SAR) automatic target recognition (ATR) application using MSTAR public SAR data, which are obtained under different depression angles, object configurations and object articulations. Experiments show the performance improvement that can obtained by considering the feature magnitudes, compared to a previous performance prediction method that only considered the locations of features. In addition, the predicted performance is compared with actual performance of a vote-based SAR recognition system using the same SAR scatterer location and magnitude features.

Optimal adaptation strategies for Golomb codes on correlated sources

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.

Extended Golomb codes for binary 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.