Image-Difference Prediction: From Grayscale to Color
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 info...
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| Veröffentlicht in: | IEEE transactions on image processing 2013-02, Vol.22 (2), p.435-446 |
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| container_title | IEEE transactions on image processing |
| container_volume | 22 |
| creator | Lissner, I. Preiss, J. Urban, P. Lichtenauer, M. S. Zolliker, P. |
| description | 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. |
| doi_str_mv | 10.1109/TIP.2012.2216279 |
| format | Article |
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Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.</description><subject>Accuracy</subject><subject>Adaptation models</subject><subject>Applied sciences</subject><subject>Color</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Image color analysis</subject><subject>image difference</subject><subject>Image processing</subject><subject>image quality</subject><subject>Indexes</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Observers</subject><subject>Predictive models</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpFkEFLw0AQRhdRbK3eBUFyEbykzuxudrPepNpaKNhDPYdNMiuRpKm77aH_3pTGepqBed8w8xi7RRgjgnlazZdjDsjHnKPi2pyxIRqJMYDk510PiY41SjNgVyF8A6BMUF2yARcAKU_4kMl5Y78ofq2cI0_rgqKlp7IqtlW7fo6mvm2imbf7UNiaom0bTdq69dfswtk60E1fR-xz-raavMeLj9l88rKICyHNNra5cJBLKFMERShyMmVpTaqdklyjQtddYUlJzHNCjSmUmmNqnDVJlxJixB6Peze-_dlR2GZNFQqqa7umdhcy5FokoCXIDoUjWvg2BE8u2_iqsX6fIWQHV1nnKju4ynpXXeS-377LGypPgT85HfDQA_bwv_N2XVThn1NpIoxQHXd35CoiOo2VAJ0qLn4B0Eh3kw</recordid><startdate>20130201</startdate><enddate>20130201</enddate><creator>Lissner, I.</creator><creator>Preiss, J.</creator><creator>Urban, P.</creator><creator>Lichtenauer, M. 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| ispartof | IEEE transactions on image processing, 2013-02, Vol.22 (2), p.435-446 |
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| subjects | Accuracy Adaptation models Applied sciences Color Detection, estimation, filtering, equalization, prediction Exact sciences and technology Feature extraction Image color analysis image difference Image processing image quality Indexes Information theory Information, signal and communications theory Observers Predictive models Signal and communications theory Signal processing Signal, noise Telecommunications and information theory |
| title | Image-Difference Prediction: From Grayscale to Color |
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