Image-difference prediction: from grayscale to color.

IEEE Trans Image Process

Institute of Printing Science and Technology, Technische Universität Darmstadt, Darmstadt 64289, Germany.

Published: February 2013

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.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TIP.2012.2216279DOI Listing

Publication Analysis

Top Keywords

image-difference measures
8
image-difference
5
image-difference prediction
4
prediction grayscale
4
grayscale color
4
color existing
4
existing image-difference
4
measures excellent
4
excellent accuracy
4
accuracy predicting
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!