Spatially adaptive wavelet thresholding with context modeling for image denoising
The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.

title: Adaptive wavelet thresholding for image denoising and compression
abstract: The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.

title: The Minimum Description Length Principle in Coding and Modeling
abstract: We review the principles of minimum description length and stochastic complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon's basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms. We assess the performance of the minimum description length criterion both from the vantage point of quality of data compression and accuracy of statistical inference. Context tree modeling, density estimation, and model selection in Gaussian linear regression serve as examples.

title: A rate of convergence result for a universal D-semifaithful code
abstract: The problem of optimal rate universal coding is considered in the context of rate-distortion theory. A D-semifaithful universal coding scheme for discrete memoryless sources is given. The main result is a refined covering lemma based on the random coding argument and the method of types. The average codelength of the code is shown to approach its lower bound, the rate-distortion function, at a rate O(n/sup -1/log n), and this is conjectured to be optimal based on a result of A.J. Pilc (1968). Issues of constructiveness and universality are addressed.

title: Wavelet thresholding via MDL for natural images
abstract: We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achieving mean-squared-error performance comparable with other popular thresholding schemes, the MDL procedure tends to keep far fewer coefficients. From this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients.

title: Wavelet thresholding for multiple noisy image copies
abstract: This correspondence addresses the recovery of an image from its multiple noisy copies. The standard method is to compute the weighted average of these copies. Since the wavelet thresholding technique has been shown to effectively denoise a single noisy copy, we consider in this paper combining the two operations of averaging and thresholding. Because thresholding is a nonlinear technique, averaging then thresholding or thresholding then averaging produce different estimators. By modeling the signal wavelet coefficients as Laplacian distributed and the noise as Gaussian, our investigation finds the optimal ordering to depend on the number of available copies and on the signal-to-noise ratio. We then propose thresholds that are nearly optimal under the assumed model for each ordering. With the optimal and near-optimal thresholds, the two methods yield similar performance, and both show considerable improvement over merely averaging.

title: Synergistic Integration of Agent Technologies for Military Simulation
abstract: To perform large-scale coordination in real-world environments requires that many individually complex technologies come together to form integrated solutions. In this paper, we present an application where several key technologies are integrated into a unified system via a multiagent infrastructure. We show how the synergistic behavior among heterogeneous technologies results in a significant improvement over the performance of the individual technologies acting alone. Critical extensions were required to the language describing required behavior to allow the pieces to work together. Initial experimental results show system performance on a task of coordinating a military convoy in an adversarial environment was significantly improved when all technologies worked together. However, experiments with a human user in the loop showed that significant advances must still be made before such systems can be fielded in the real-world

title: An Efficient Zoning Technique for Multi-dimensional Access Methods
abstract: In emerging database applications that deal with large sets of multidimensional data, the performance of the query system significantly depends on the performance of its access methods and the underlying disk system. In recent years, hard disks are manufactured with multiple physical zones, where seek times and data transfer rates vary significantly across the zones. However, there is a marked lack of investigation on how to optimize multidimensional access methods given a zoned disk model. The paper proposes a novel dynamic zoning technique called DMD-Zoning that can be applied to a variety of multidimensional access methods and that can fully utilize zoning characteristics of hard disks for busy multi-user database systems.

title: An evidential model of distributed reputation management
abstract: For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management.Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.

title: Inverted-space storage organization for persistent data of very high dimensionality
abstract: Contemporary database technology is severely limited in managing the high-dimensional data of many advanced applications, such as multimedia systems and data mining. The main concern of this paper is the well-known performance degradation of multi-dimensional access methods in spaces with many dimensions. The paper proposes an elaborate storage organization, called the inverted space, which can support efficient processing of data in spaces with very high dimensionality. The organization allows system administrators to control the size of spatial indexes and thereby to avoid the negative impact of extremely high data dimensionality on the retrieval performance. In addition, this paper introduces a new point access method designed to address numerous other problems that contemporary retrieval schemes experience in high-dimensional situations. This mechanism is envisioned to serve as the core indexing structure of inverted-space storage organizations.

title: Implementing KDB-trees to support high-dimensional data
abstract: The problem of retrieving large volumes of high dimensional data is an important and timely issue in the area of database management. The guiding idea of the paper is to develop a general-purpose point access method that attacks the limitations of KDB-trees in high-dimensional spaces, while preserving their relatively good performance in low-dimensional situations. The proposed structure, called high-dimensional KDB-tree, eliminates downward propagation of splits associated with the original KDB-tree structure, which results in low storage utilization and rapid deterioration of the retrieval performance. Additional improvements in the storage and retrieval performance are achieved by removing certain redundant information from the interior nodes. Experimental results show that, in high-dimensional spaces, the proposed structure outperforms the original KDB-trees by a significant margin, while incurring no loss of performance in low-dimensional spaces. The structure also outperforms two other variants of KDB-trees investigated in the paper.