Overlapping tiling for fast random access of low-dimensional data from high-dimensional datasets

Fan, Zihong; Ortega, Antonio

doi:10.1117/12.805419

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…The main advantage of this approach is that it lowers the encoding/decoding complexity, but at the cost of introducing some distortion (i.e., the pixel values that are aligned for encoding were not aligned in the original representation). In our 3D case study, 3D compression has not been applied yet, however experimental results using compression in the 2D case with the same mapping algorithm [11] demonstrate that the symmetric mapping algorithm leads to better RD performance than a non-symmetric approach and both the mapping algorithms lead better RD performance than the interpolation method. A situation analogous to that observed in compression of Bayer filter images [12] arises: interpolation leads to smoother images, but also removes information from the original data so that at high rates re-mapping leads to better performance than interpolation.…”

Section: B Mapping Algorithm For Rotated Tile Encodingmentioning

confidence: 99%

“…Note that several solutions may be optimal (different rectangle combinations that cover the plane, using the same number of rectangles). Our 3D rectangle search algorithm is similar to our previously proposed 2D rectangle search algorithm [11]. Instead of sorting the points associated with a line as in the 2D case in [11], the points on a plane are sorted in order of decreasing distance to the center point of the plane (this sorted list of points is called List A.)…”

Section: A Fast 3d Rectangle Search Algorithmmentioning

confidence: 99%

“…Our 3D rectangle search algorithm is similar to our previously proposed 2D rectangle search algorithm [11]. Instead of sorting the points associated with a line as in the 2D case in [11], the points on a plane are sorted in order of decreasing distance to the center point of the plane (this sorted list of points is called List A.) In Algorithm 1, we start by finding the tile that covers the first element of List A (the furtherest point from the center of the plane) and covers the largest number of points in the list.…”

Section: A Fast 3d Rectangle Search Algorithmmentioning

confidence: 99%

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Overlapped Tiling for Fast Random Oblique Plane Access of 3D Object Datasets

Fan

Ortega

Chien³

2009

2009 Data Compression Conference

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Volume visualization with random data access poses significant challenges. While tiling techniques lead to simple implementations, they are not well suited for cases where the goal is to access arbitrarily located subdimensional datasets (e.g., being able to display an arbitrary 2D planar "cut" from a 3D volume). Significant effort has been devoted to volumetric data compression, with most techniques proposing to tile volumes into cuboid subvolumes to enable random access. In this paper we show that, in cases where subdimensional datasets are accessed, this leads to significant transmission inefficiency. As an alternative, we propose novel serverclient based data representation and retrieval methods which can be used for fast random access of oblique plane from 3D volume datasets. In this paper, 3D experiments are shown but the approach may be extended to higher dimensional datasets. We use multiple redundant tilings of the 3D object, where each tiling has a different orientation. We discuss the 3D rectangular tiling scheme and two main algorithm components of such 3D system, namely, (i) a search algorithm to determine which tiles should be retrieved for a given query and (ii) a mapping algorithm to enable efficient encoding without interpolation of rotated tiles. In exchange for increased server storage, we demonstrate that significant reductions in average transmission rate can be achieved relative to conventional cubic tiling techniques, e.g., nearly 40% reduction in average transmission rate for less than a factor of twenty overhead in storage before compression. Note that, as shown in our earlier work on the 2D case, the storage overhead will be lower after compression (e.g., in 2D the relative increase in storage in the compressed domain was at least a factor of two lower than in the uncompressed domain.) I. INTRODUCTION In many fields (e.g., biomedical imaging, earth sciences, computational fluid dynamics, etc.) researchers need to manipulate and visualize very large datasets. Often it is not practical for a personal computer to be equipped with sufficient memory so as to enable manipulation, visualization and rendering of the complete dataset. In these kinds of applications, a client-server approach can be more effective, with the server providing only the data needed for the specific visualization task at the client. Such client-server approaches are widely used [1]-[6] , e.g., for interactive viewing of maps at various resolutions, and are often supported by tiling techniques, so that the server provides only those tiles corresponding to data requested by the client.Tiling-based techniques are efficient when most of the information included in the tiles is used for display. This is the case, for example, when a 2D region of a larger 2D dataset (e.g., a map) is to be displayed or when small sub-volumes of a large 3D volume are needed [2] (i.e., the dimension of the subset and the dataset from which it is extracted are equal). In this paper we tackle a more challenging problem for which conventio...

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Section: B Mapping Algorithm For Rotated Tile Encodingmentioning

confidence: 99%

Section: A Fast 3d Rectangle Search Algorithmmentioning

confidence: 99%