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Diffstat (limited to 'site/content/gsoc/method_comparison.typ')
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1 files changed, 4 insertions, 4 deletions
diff --git a/site/content/gsoc/method_comparison.typ b/site/content/gsoc/method_comparison.typ index f6219e7..db6b4df 100644 --- a/site/content/gsoc/method_comparison.typ +++ b/site/content/gsoc/method_comparison.typ @@ -7,8 +7,8 @@ I took the dataset published alongside Otsu et al. (2018) @Otsu2018 and #link("https://colour.readthedocs.io/en/develop/generated/colour.COLOURCHECKER_SDS.html")[colorcheckers included in Colour], about 1300 reflectance spectra in total, reconstructed them using various methods and compared them with the original data. I used #link("https://en.wikipedia.org/wiki/Illuminant_D65")[CIE Standard Illuminant D65] and two metrics: -- CIE 1976 ΔE (ΔE is defined as the Euclidean distance between colors in CIELAB color space.), quantizing the *perceived* difference between two colors. Values around 2.4 are taken as the just-noticeable difference, so deltas smaller than this should be insignificant. -- Mean squared error (MSE), the mean squared difference between the original and reconstructed spectra. For any reflectance, there infinitely many metamers, other reflectances that look exactly the same (ΔE=0) under a given illuminant. Good spectral upsampling should result in *realistic* spectra, i.e. those that look similar to those of common and natural materials. This helps spectral rendering to achieve natural results as well. See Peters et al. (2019) @Peters2019 for an interesting and more in-depth discussion of the topic. +- CIE 1976 ΔE (ΔE is defined as the Euclidean distance between colors in CIELAB color space.), quantifying the *perceived* difference between two colors. Values around 2.4 are taken as the just-noticeable difference, so deltas smaller than this should be insignificant. +- Mean squared error (MSE), the mean squared difference between the original and reconstructed spectra. For any reflectance, there are infinitely many metamers, other reflectances that look exactly the same (ΔE=0) under a given illuminant. Good spectral upsampling should result in *realistic* spectra, i.e. those that look similar to those of common and natural materials. This helps spectral rendering to achieve natural results as well. See Peters et al. (2019) @Peters2019 for an interesting and more in-depth discussion of the topic. Below are chromaticity diagrams with error heatmaps drawn over them. The color of each square bin represents the mean error of samples lying inside the square. The numbers above the diagrams are, in order, the minimum, maximum, mean and standard deviation of the errors. @@ -34,7 +34,7 @@ Letting SciPy's `L-BFGS-B` solver decide on its own when to stop actually makes caption: [Errors for Otsu et al. (2018) with `clip` set to False.], ) -The picture above shows that the method has no round-trip errors, ie. the recovered spectra are exact metamers (have the exact same color). +The picture above shows that the method has no round-trip errors, i.e., the recovered spectra are exact metamers (have the exact same color). #figure( img(static("gsoc/error_otsu.jpg")), @@ -79,6 +79,6 @@ Since all methods (bugs and user errors aside) have negligible ΔE's, I'll summa [Jakob and Hanika (2019)], [0.029 2], [60 ×], [0.152], [0.032], ) -Unsurprisingly, the data-driven method produces results that look like data the most. Jakob and Hanika's equation seems model much more artificial reflectances. +Unsurprisingly, the data-driven method produces results that look like data the most. Jakob and Hanika's equation seems to model much more artificial reflectances. #bibliography("/refs.bib") |