@inproceedings{Kurt2020MAM,
 author = {Kurt, Murat},
 title = {{A Genetic Algorithm Based Heterogeneous Subsurface Scattering Representation}},
 booktitle = {Proceedings of the 8th Eurographics Workshop on Material Appearance Modeling: Issues and Acquisition},
 series = {MAM '20},
 year = {2020},
 isbn = {978-3-03868-108-3},
 pages = {13-16},
 numpages = {4},
 url = {https://diglib.eg.org/handle/10.2312/mam20201140},
 doi = {10.2312/mam.20201140},
 editor = {Reinhard Klein and Holly Rushmeier},
 address = {London, UK},
 publisher = {The Eurographics Association},
 abstract = {In this paper, we present a novel heterogeneous subsurface scattering (sss) representation, which is based on a combination
of Singular Value Decomposition (SVD) and genetic optimization techniques. To find the best transformation that is applied
to measured subsurface scattering data, we use a genetic optimization framework, which tries various transformations to the
measured heterogeneous subsurface scattering data to find the fittest one. After we apply the best transformation, we compactly
represent measured subsurface scattering data by separately applying the SVD per-color channel of the transformed profiles.
In order to get a compact and accurate representation, we apply the SVD on the model errors, iteratively. We validate our
approach on a range of optically thick, real-world translucent materials. It’s shown that our genetic algorithm based heterogeneous
subsurface scattering representation achieves greater visual accuracy than alternative techniques for the same level of
compression.}
}