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Although subjective tests are most accurate image/video quality assessmenttools, they are extremely time demanding. In the past two decades, a variety ofobjective tools, such as SSIM, IW-SSIM, SPSIM, FSIM, etc., have been devised,that well correlate with the subjective tests results. However, the mainproblem with these methods is that, they do not discriminate the measuredquality well enough, especially at high quality range. In this article we showhow the accuracy/precision of these Image Quality Assessment (IQA) meters canbe increased by mapping them into a Logistic Function (LF). The precisions aretested over a variety of image/video databases. Our experimental tests indicatewhile the used high-quality images can be discriminated by 23% resolution onthe MOS subjective scores, discrimination resolution by the widely used IQAsare only 2%, but their mapped IQAs to Logistic Function at this quality rangecan be improved to 9.4%. Moreover, their precision at low to mid quality rangecan also be improved. At this quality range, while the discriminationresolution of MOS of the tested images is 23.2%, those of raw IQAs is nearly8.9%, but their adapted logistic functions can lead to 17.7%, very close tothat of MOS. Moreover, with the used image databases the Pearson correlation ofMOS with the logistic function can be improved by 2%-20.2% as well.

Improving precision of objective image/video quality metrics