Color-Image Quality Assessment From Prediction to Optimization
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 colorimage-
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.
KeyWords
ImageDifference,imageQuality,color,gamutmapping
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