A compression method for a massive image data set in image-based rendering ?
In image-based rendering with adjustable illumination, the data set contains a large number of pre-captured images under different sampling lighting directions. Instead of individually compressing each pre-captured image, we propose a two-level compression method. Firstly, we use a few spherical harmonic (SH) coefficients to represent the plenoptic property of each pixel. The classical discrete summation method for extracting SH coefficient requires that the sampling lighting directions should be uniformly distributed on the whole spherical surface. It cannot handle the case that the sampling lighting directions are irregularly distributed. A constrained least-squares algorithm is proposed to handle this case. Afterwards, embedded zero-tree wavelet coding is used for removing the spatial redundancy in SH coefficients. Simulation results show our approach is much superior to the JPEG, JPEG2000, MPEG2, and 4D wavelet compression method. The way to allow users to interactively control the lighting condition of a scene is also discussed.
massive image data set spherical harmonics wavelets