Revisiting gamut expansion for color space conversion

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dc.contributor.advisorBrown, Michael S.
dc.contributor.authorLe, Hoang Minh
dc.date.accessioned2024-11-07T11:17:00Z
dc.date.available2024-11-07T11:17:00Z
dc.date.copyright2024-09-19
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:16:57Z
dc.degree.disciplineElectrical Engineering & Computer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractCameras and image-editing software are capable of processing images in the wide-gamut ProPhoto color space, which encompasses 90\% of all visible colors. However, when images are prepared for sharing, this rich color representation is transformed and clipped to fit within the smaller-gamut standard RGB (sRGB) color space, which represents only 30\% of visible colors. Recovering the lost color information poses a challenge due to this clipping procedure. We propose three methods to address this issue. The first method proposes a deep neural network specifically trained for wide-gamut color restoration, utilizing datasets generated by a software-based camera image signal processor that produces pairs of ProPhoto and sRGB images. The second method implements a technique that incorporates a small set of color values sampled from the original ProPhoto image that is saved together with the final smaller-gamut sRGB image. The method then uses the subsampled wide-gamut color values to estimate the original ProPhoto image from the sRGB image. The third method proposes a lightweight multi-layer perceptron (MLP) trained on pairs of ground truth and clipped ProPhoto values during the gamut compression phase. The MLP is saved as metadata in the sRGB image and can later be used to predict and restore the original wide-gamut colors during the gamut expansion phase. Additionally, we have created several large-scale public datasets of wide-gamut/small-gamut image pairs to support research on color space conversion.
dc.identifier.urihttps://hdl.handle.net/10315/42501
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectComputer engineering
dc.subject.keywordsImage processing
dc.subject.keywordsComputational color
dc.subject.keywordsComputer vision
dc.subject.keywordsDeep learning
dc.subject.keywordsMachine learning
dc.subject.keywordsCamera pipeline
dc.subject.keywordsGamut mapping
dc.subject.keywordsGamut restoration
dc.subject.keywordsGamut expansion
dc.subject.keywordsColor space conversion
dc.subject.keywordsMulti layer perceptrons
dc.subject.keywordsColor science
dc.titleRevisiting gamut expansion for color space conversion
dc.typeElectronic Thesis or Dissertation

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