Nonuniform Lattice Regression for Modeling the Camera Imaging Pipeline
Abstract
We describe a method to construct a sparse lookup table (LUT) that is effective in modeling the camera imaging pipeline that maps a RAW camera values to their sRGB output. This work builds on the recent in-camera color processing model proposed by Kim et al. [1] that included a 3D gamut-mapping function. The major drawback in [1] is the high computational cost of the 3D mapping function that uses radial basis functions (RBF) involving several thousand control points. We show how to construct a LUT using a novel nonuniform lattice regression method that adapts the LUT lattice to better fit the 3D gamut-mapping function. Our method offers not only a performance speedup of an order of magnitude faster than RBF, but also a compact mechanism to describe the imaging pipeline.
Cite
Text
Lin et al. "Nonuniform Lattice Regression for Modeling the Camera Imaging Pipeline." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33718-5_40Markdown
[Lin et al. "Nonuniform Lattice Regression for Modeling the Camera Imaging Pipeline." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/lin2012eccv-nonuniform/) doi:10.1007/978-3-642-33718-5_40BibTeX
@inproceedings{lin2012eccv-nonuniform,
title = {{Nonuniform Lattice Regression for Modeling the Camera Imaging Pipeline}},
author = {Lin, Hai Ting and Lu, Zheng and Kim, Seon Joo and Brown, Michael S.},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {556-568},
doi = {10.1007/978-3-642-33718-5_40},
url = {https://mlanthology.org/eccv/2012/lin2012eccv-nonuniform/}
}