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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| Vydáno v: | IEEE transactions on circuits and systems for video technology Ročník 15; číslo 3; s. 355 - 364 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.03.2005
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1051-8215, 1558-2205 |
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| Abstract | 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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| AbstractList | [...] we propose a real-time engine that relights images from the compressed data. 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. |
| Author | Tien-Tsin Wong Chi-Sing Leung Pun-Mo Ho |
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| Cites_doi | 10.7551/mitpress/2002.003.0004 10.1109/CVPR.1996.517085 10.1109/76.836278 10.1109/38.909013 10.1109/TMM.2002.802835 10.1007/978-3-7091-6858-5_2 10.1109/TCSVT.2003.817628 10.1145/218380.218395 10.1109/iccv.2001.937606 10.1145/383745.383747 10.1109/CVPR.1999.787003 10.1109/pbmcv.1995.514675 10.1109/TCOM.1983.1095893 10.1145/237170.237200 10.1109/DCC.2000.838165 10.1145/237170.237199 10.1007/978-1-4613-1643-5 10.1145/311535.311573 |
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| Keywords | 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 |
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| Snippet | The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the... [...] we propose a real-time engine that relights images from the compressed data. |
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| SubjectTerms | 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 |
| Title | Compressing the illumination-adjustable images with principal component analysis |
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