Compressing the Illumination-Adjustable Images with Principal Component Analysis
Compressing the Illumination-Adjustable Images with Principal Component Analysis
Pun-Mo Ho

The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the trade-off is the enormous increase of storage requirement. In this thesis, 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 proposed real-time engine that relights images from the compressed data.

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