Viewing distance and image resolution have substantial influences on image quality assessment (IQA), but this issue has been highly overlooked in the literature so far. In this paper, we examine the problem of optimal resolution adjustment as a preprocessing step for IQA. In general, the sampling of visual information by human eyes’ optics is approximately a low-pass process. For a given visual scene, the amount of the extractable information greatly depends on the viewing distance and image resolution. We first introduce a novel dedicated viewing distance-changed image database (VDID2014) with two groups of typical viewing distances and image resolutions to promote the IQA study for this issue.
Then we design a new effective optimal scale selection (OSS) model in dual-transform domains, in which a cascade of adaptive high-frequency clipping in the discrete wavelet transform domain and adaptive resolution scaling in the spatial domain is used. Validation of our technique is conducted on five imagedatabases (LIVE, IVC, Toyama, VDID2014, and TID2008). Experimental results show that the performance of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) can be substantially improved by applying these metrics to OSS model preprocessed images, superior to classical multi-scale-PSNR/SSIM and comparable to the state-of-the-art competitors.