Improving precision of objective image/video quality metrics

TL;DR

This paper enhances the precision of existing objective image/video quality metrics by applying a logistic function mapping, significantly improving their discrimination ability across different quality ranges and better aligning with subjective assessments.

Contribution

It introduces a novel mapping method using logistic functions to improve the accuracy and discrimination power of existing IQA metrics across various quality levels.

Findings

Discrimination resolution improved from 2% to 9.4% at high quality range.

Precision at low to mid quality range increased to 17.7%.

Pearson correlation with subjective scores improved by up to 20.2%.

Abstract

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.