Eigen-image based compression for the image-based relighting with cascade recursive least squared networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1219-1231
Journal / PublicationPattern Recognition
Issue number6
Publication statusPublished - Jun 2004


This paper presents a principal component analysis (PCA) based data compression method for the image-base relighting (IBL) technology, which needs tremendous reference images to produce high quality rendering. The method contains two main steps, eigen-image based representation and eigen-image compression. We extract eigen-images by the cascade recursive least squared (CRLS) networks based PCA due to the large data dimension. By keeping only a few important eigen-images, which are enough to describe the IBL data set, the data size can be drastically reduced. To further reduce the data size, we use the embedded zero wavelet (EZW) approach to compress those retained eigen-images, and use uniform quantization plus arithmetic coding to compress the representing coefficients. Simulation results demonstrate that our approach is superior to that of compressing reference images separately with JPEG or EZW. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • Cascade recursive least squared (CRLS), Data compression, Image-based relighting, Principal component analysis, Wavelets