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• W1970383695 abstract "While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications." @default.
• W1970383695 created "2016-06-24" @default.
• W1970383695 creator A5002126352 @default.
• W1970383695 creator A5026357081 @default.
• W1970383695 creator A5070239231 @default.
• W1970383695 date "2014-03-01" @default.
• W1970383695 modified "2023-10-02" @default.
• W1970383695 title "Color-Image Quality Assessment: From Prediction to Optimization" @default.
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• W1970383695 doi "https://doi.org/10.1109/tip.2014.2302684" @default.
• W1970383695 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24723533" @default.
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