Hybrid Objective Metric for Image Quality Assessment

D. V. N. Koteswara Rao, Gutti Prasad, Rahul Lakkakula

Abstract: Image Quality Assessment (IQA) goal is to use computational models to measure the image quality consistently with subjective evaluations. In this paper, a peculiar feature-similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) perceives an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimension less measure of significance of a local structure, is used as the primary feature. Considering that PC is unaffected by contrast, while the contrast information does affect the HVS’ perception of image quality, the image gradient magnitude is employed as the secondary feature in FSIM. After obtaining the local quality map, we use PC again as a weighting function to derive single quality score. Extensive experiments performed on TID2008, a widely using bench mark IQA database demonstrated that FSIM can achieve much higher consistency with the subjective evaluations than state-of- the -art IQA metrics.

Keywords: Image quality assessment, phase congruency, gradient, low-level feature

How to Cite?: D. V. N. Koteswara Rao, Gutti Prasad, Rahul Lakkakula, "Hybrid Objective Metric for Image Quality Assessment", Volume 2 Issue 4, April 2013, International Journal of Science and Research (IJSR), Pages: 323-327, https://www.ijsr.net/getabstract.php?paperid=IJSRON1201315, DOI: https://dx.doi.org/10.21275/IJSRON1201315