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 language | English |
|---|---|
| Pages (from-to) | 1291-1300 |
| Journal | Neural Computing and Applications |
| Volume | 22 |
| Issue number | 7-8 |
| DOIs | |
| Publication status | Published - Jun 2013 |
Research Keywords
- HEALPIX
- Illumination adjustable images
- Image-based rendering
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