title: RPW: a hybrid reverse prediction method for level of detail.
abstract: In this paper, we propose a way of using multiple domain analysis methodology to speed up the image rendering process. We first apply wavelet transform to the original image, and then compress the wavelets in the frequency domain using histogram equalization techniques. When rendering the image, we uncompress the wavelets and reverse predict the upper level images. This process continues until it reaches a certain criteria. We use two terms-total image size (TIS) and total loading time (TLT) to measure the performance of level of detail (LOD) in a network environment. We compare traditional image-based LOD methods with the new method we are proposing. Experiments show that the proposed method can reduce both TIS and TLT. The image rendering speed on a remote client is approximately 2.5 times faster than the common image compression methods. Applications such as remote diagnostic systems and online museums can use this technique to achieve better real-time animation effects.
authors.affiliation: George Mason University, 4400 University Drive, MS 1F2, Fairfax, VA 22030, United States. swang3@gmu.edu
authors.forename: Shuangbao
authors.initials: S
authors.lastname: Wang
authors.forename: Zegang
authors.initials: Z
authors.lastname: Dong
authors.forename: Robert S
authors.initials: RS
authors.lastname: Ledley
mesh_terms: D000465:Algorithms; D005583:Fourier Analysis; D008962:Models, Theoretical; D012680:Sensitivity and Specificity