Multilevel Focus+Context Visualization Using Balanced Multiresolution

In this paper, we present the construction of a multilevel focus context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution (BMR) technique supported by a balanced wavelet transform (BWT). This devised framework extends the mode of focus context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. We use the local multiresolution filters of quadratic B-spline to construct the BWT. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. Changes in the spatial locations of query windows that define the ROIs trigger on-demand reconstruction queries. We describe in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization and exploration of complex large-scale imagery.