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Pei, Soo-Chang; Chen, Li-Heng
IEEE transactions on image processing, 2015-Nov., 2015-Nov, 2015-11-00, Letnik: 24, Številka: 11Journal Article
Objective image quality assessment (IQA) plays an important role in the development of multimedia applications. Prediction of IQA metric should be consistent with human perception. The release of the newest IQA database (TID2013) challenges most of the widely used quality metrics (e.g., peak-to-noise-ratio and structure similarity index). We propose a new methodology to build the metric model using a regression approach. The new IQA score is set to be the nonlinear combination of features extracted from several difference of Gaussian (DOG) frequency bands, which mimics the human visual system (HVS). Experimental results show that the random forest regression model trained by the proposed DOG feature is highly correspondent to the HVS and is also robust when tested by available databases.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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