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Image-difference prediction: from grayscale to color.
- Source :
-
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2013 Feb; Vol. 22 (2), pp. 435-46. Date of Electronic Publication: 2012 Sep 19. - Publication Year :
- 2013
-
Abstract
- Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
Details
- Language :
- English
- ISSN :
- 1941-0042
- Volume :
- 22
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publication Type :
- Academic Journal
- Accession number :
- 23008252
- Full Text :
- https://doi.org/10.1109/TIP.2012.2216279