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HEALPIX DCT technique for compressing PCA-based illumination adjustable images

John Sum, Chi-Sing Leung, Ray C.C. Cheung, Tze-Yiu Ho

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

Abstract

An illumination adjustable image (IAI), containing a set of pre-captured reference images under various light directions, represents the appearance of a scene with adjustable illumination. One of drawbacks of using the IAI representation is that an IAI consumes a lot of memory. Although some previous works proposed to use blockwise principal component analysis for compressing IAIs, they did not consider the spherical nature of the extracted eigen-coefficients. This paper utilizes the spherical nature of the extracted eigen-coefficients to improve the compression efficiency. Our compression scheme consists of two levels. In the first level, the reference images are converted into a few eigen-images (floating point images) and a number of eigen-coefficients. In the second level, the eigen-images are compressed by a wavelet-based method. The eigen-coefficients are organized into a number of spherical functions. Those spherical coefficients are then compressed by the proposed HEALPIX discrete cosine transform technique. © 2012 Springer-Verlag London Limited.
Original languageEnglish
Pages (from-to)1291-1300
JournalNeural Computing and Applications
Volume22
Issue number7-8
DOIs
Publication statusPublished - Jun 2013

Research Keywords

  • HEALPIX
  • Illumination adjustable images
  • Image-based rendering

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