Noise-Resistant Fitting for Spherical Harmonics
IEEE Transactions on Visualization and Computer Graphics
Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-intensive image based relighting
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
Eigen combination of colour and texture informations for image segmentation
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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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.