Abstract
Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR performance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of super-resolved images (SRIs). In constructing the quality assessment database for SRIs (QADS), we carefully selected 20 reference images and created 980 SRIs using 21 image SR methods. Mean opinion score (MOS) for these SRIs is collected through 100 individuals participating in a suitably designed psychovisual experiment. Extensive numerical and statistical analysis is performed to show that the MOS of QADS has excellent suitability and reliability. The psychovisual experiment has led to the discovery that, unlike distortions encountered in other IQA databases, artifacts of the SRIs degenerate the image structure as well as the image texture. Moreover, the structural and textural degenerations have distinctive perceptual properties. Based on these insights, we propose a novel method to assess the visual quality of SRIs by separately considering the structural and textural components of images. Observing that textural degenerations are mainly attributed to dissimilar texture or checkerboard artifacts, we propose to measure the changes of textural distributions. We also observe that structural degenerations appear as blurring and jaggies artifacts in SRIs and develop separate similarity measures for different types of structural degenerations. A new pooling mechanism is then used to fuse the different similarities together to give the final quality score for an SRI. The experiments conducted on the QADS demonstrate that our method significantly outperforms the classical as well as current state-of-the-art IQA methods.
Highlights
IMAGE super-resolution (SR) is an important research problem in the field of image processing
Since textural degenerations mainly manifest as dissimilar texture or checkerboard artifacts, we propose to measure the changes of textural distributions to take into account both types of textural artifacts
We focus on FR image quality assessment (IQA) for super-resolved images (SRIs)
Summary
IMAGE super-resolution (SR) is an important research problem in the field of image processing. It is extensively used in many applications, including high definition television, security surveillance, coding and transmission [1], etc. The aim of SR is to generate images with higher spatial resolution that are free from aliasing and other artifacts. One of the key challenges to the development of SR techniques is the assessment of the visual quality of the super-resolved images (SRIs). This paper fills a gap in the image SR literature by providing a carefully designed database and a state-of-the-art visual quality assessment method for image SR research
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