Designing a biased specification-based subjective test of image quality

Abstract

Specification-based subjective tests (SBSTs) form the basis for almost all performance analysis of image and video quality estimators (QEs). Our ability to compare the efficacy of different QEs across a wide range of applications depends on careful design of these SBST, so the conclusions that are drawn about how well a QE performs are reliable and accurate. In this paper, we explore methods to design biased SBSTs for image and video QEs. A biased SBST will produce an estimate of the performance of a given QE that is systematically different than its actual performance. We demonstrate by proof of concept that it's possible to create SBSTs that generate misleading or biased Pearson or Spearman correlation coefficients between subjective and objective scores, and we present some diagnostics that begin to evaluate when influential observations have been included in the SBST. Understanding how to create biased tests is a first step toward the overall goal of creating more effective unbiased SBSTs.