1. Image-Difference Prediction: From Grayscale to Color.
- Author
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Lissner, Ingmar, Preiss, Jens, Urban, Philipp, Lichtenauer, Matthias Scheller, and Zolliker, Peter
- Subjects
- *
IMAGE compression , *IMAGE processing , *PERFORMANCE evaluation , *FEATURE extraction , *PREDICTION models , *IMAGE quality analysis , *INFORMATION theory - 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. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
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