  Although subjective tests are most accurate image/video quality assessment
tools, they are extremely time demanding. In the past two decades, a variety of
objective tools, such as SSIM, IW-SSIM, SPSIM, FSIM, etc., have been devised,
that well correlate with the subjective tests results. However, the main
problem with these methods is that, they do not discriminate the measured
quality well enough, especially at high quality range. In this article we show
how the accuracy/precision of these Image Quality Assessment (IQA) meters can
be increased by mapping them into a Logistic Function (LF). The precisions are
tested over a variety of image/video databases. Our experimental tests indicate
while the used high-quality images can be discriminated by 23% resolution on
the MOS subjective scores, discrimination resolution by the widely used IQAs
are only 2%, but their mapped IQAs to Logistic Function at this quality range
can be improved to 9.4%. Moreover, their precision at low to mid quality range
can also be improved. At this quality range, while the discrimination
resolution of MOS of the tested images is 23.2%, those of raw IQAs is nearly
8.9%, but their adapted logistic functions can lead to 17.7%, very close to
that of MOS. Moreover, with the used image databases the Pearson correlation of
MOS with the logistic function can be improved by 2%-20.2% as well.
