ABSTRAmThis paper is concerned with remote browsing of JPEG2000 compressed imagery. A potentially interactive client identifies a region and maximum resolution of interest. The server responds by sending information from the JPEG2000 code-stream which is relevant to this region and resolution. We propose an algorithm for sequencing data fiom the original wde-stream in such a way as to maximize the received image quality within the region of interest, at each point in the transmission. The proposed R-D optimal sequencing algorithm is demonstrated in the context of two quite different client-server paradigms. one of which i s consistent with the evolving JPlP (JPEG2000 internet protocols) standard. Performance improvements as large as 8 dB are achieved with respect to a layer progressive sequencing stratea.
Researchers and analysts in modern industrial and academic environments are faced with a daunting amount of multi-dimensional data. While there has been significant development in the areas of data mining and knowledge discovery, there is still the need for improved visualizations and generic solutions. The state-of-the-art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on fast interactive abilities. The common trend in these scenarios is to either visualize an abstraction of the data set or to better utilize screen-space. This paper presents a novel technique that combines clustering, dimension reduction and multi-dimensional data representation to form a multivariate data visualization that incorporates both detail and overview. This amalgamation counters the individual drawbacks of common projection and multi-dimensional data visualization techniques, namely ambiguity and clutter. A specific clustering criterion is used to decompose a multi-dimensional data set into a hierarchical tree structure. This decomposition is embedded in a novel Dimensional Anchor visualization through the use of a weighted linear dimension reduction technique. The resulting Structural Decomposition Tree (SDT) provides not only an insight of the data set's inherent structure, but also conveys detailed coordinate value information. Further, fast and intuitive interaction techniques are explored in order to guide the user in highlighting, brushing, and filtering of the data.
Abstract. The presentation of large hierarchies is still an open research question. Especially, the time-consuming calculation of the visualization and the cluttered display lead to serious usability issues on the viewer side. Existing solutions mainly address appropriate visual representation and usually neglect considering system resources. We propose a holistic approach for the presentation of large hierarchies using treemaps and progressive refinement. The key feature of the approach is the mature use of multiple incremental previews of the data. These previews are well-designed and lead to reduced visual clutter and a causal flow in terms of a tour-through-the-hierarchy. The inherent scalability of the data thereby allows for a reduction in the consumed resources and short response times. These characteristics are substantiated by the results we achieved from a first implementation. Due to its many beneficial properties, we conclude that there is much potential for the use of progressive refinement in visualization.
Abstract. Literature concerning the visualization of abstract data in immersive environments is sparse. This publication is intended to (1) stimulate the application of abstract data visualization in such environments and to (2)
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