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

Abstract

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...

Optimization of Overlapped Tiling for Efficient 3D Image Retrieval

“…Then, based on the insights obtained from the proposed model, a new 3D rectangular tiling scheme is proposed, which leads to better performance and verifies the model. We demonstrate that in exchange for increased server-side storage, significant reductions in average transmission rate can be achieved relative to both the conventional cubic tiling techniques and our previously proposed approach [10]. The run-time memory space can also be reduced accordingly.…”

“…In our previous work [10], we were able to achieve reductions in transmission rate of 15% -50%, as compared to the conventional tiling scheme, depending on the desired trade-off with the storage overhead. For a fixed storage overhead, different values of distance between rotation centers and the number of rotation tiles around each rotation center achieve different levels of transmission efficiency.…”

“…These approaches can be inefficient in the scenario we focus on, because the only useful voxels 1 for each retrieved cubic tile are those near the intersection between the cube and the desired 2D plane. In our previous work [10], we showed that it is more efficient to use overlapping rotated tiles to represent the dataset, which leads to an increase in the average number of useful voxels per tile so that the total number of tiles to be retrieved is smaller (and hence a lower transmission bitrate is achieved). In this approach, we trade-off increased storage at the server's side for lower bandwidth during interactive access to the data-set.…”

“…2 and consists of 5 components: 1) 3D rectangular tiling scheme, 2) mapping algorithm, 3) tile searching algorithm, 4) 3D compression, and 5) random oblique plane reconstruction and display. We have proposed a 3D rectangular tiling scheme and discussed components (2) and (3) in [10]. The tiling scheme provides multiple redundant tilings of the 3D object, where each tile has a different orientation and is associated with one rotation center.…”

“…We start by providing a prediction model for parameter selection in Section II, followed by presenting a new 3D rectangular tiling scheme in Section III. Then in Section IV, a performance comparison of the proposed 3D rectangular tiling scheme with the previous tiling scheme [10] is provided. The new tiling scheme achieves an additional 10% -30% reduction in the average transmission rate compared to the previous rectangular tiling scheme.…”