Experimental study reveals standard objective metrics fail to reflect subjective quality in super-resolution images, highlighting the need for artifact-aware assessment models.
A typical image formation model for super-resolution (SR) introduces blurring, aliasing, and added noise. The enhancement itself may also introduce ringing. In this paper, we use subjective tests to assess the visual quality of SR-enhanced images. We then examine how well some existing objective quality metrics can characterize the observed subjective quality. Even full-reference metrics like MSE and SSIM do not always capture visual quality of SR images with and without residual aliasing.
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Reibman et al. (2006) studied this question.
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