Compressing the illumination-adjustable images with principal component analysis

The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the tradeoff is the enormous increase of storage requirement. In this paper, we propose a compression scheme that effectively reduces the data volume...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 15; no. 3; pp. 355 - 364
Main Authors: HO, Pun-Mo, WONG, Tien-Tsin, LEUNG, Chi-Sing
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Artificial intelligence
Bit rate
Compressed
Compressing
Computer science
Computer science; control theory; systems
Data compression
Exact sciences and technology
Image coding
Image processing
image-based modeling and rendering
image-based relighting
Information, signal and communications theory
Layout
Lighting
Mathematical models
Navigation
Pattern recognition. Digital image processing. Computational geometry
plenoptic illumination function
Principal component analysis
Principal components analysis
Quantization
Real time
Rendering
Rendering (computer graphics)
Signal processing
Studies
Telecommunications and information theory
Transform coding
Image processing
Data compression
plenoptic illumination function
Illumination
Image rendering
Image compression
Modeling
image-based modeling and rendering
Divide and conquer method
image-based relighting
Principal component analysis
ISSN:1051-8215, 1558-2205
Online Access:Get full text
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Description
Summary:The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the tradeoff is the enormous increase of storage requirement. In this paper, we propose a compression scheme that effectively reduces the data volume while maintaining the real-time relighting capability. The proposed method is based on principal component analysis (PCA). A block-wise PCA is used to practically process the huge input data. The output of PCA is a set of eigenimages and the corresponding relighting coefficients. By dropping those low-energy eigenimages, the data size is drastically reduced. To further compress the data, eigenimages left are compressed using transform coding and quantization while the relighting coefficients are compressed using uniform quantization. We also suggest the suitable target bit rate for each phase of the compression method in order to preserve the visual quality. Finally, we propose a real-time engine that relights images from the compressed data.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2004.842601