Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge. 1. Introduction Digital imaging and image-processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. Today, we have come to expect the ability to instantly share photos online, to send and receive multimedia MMS messages at a moment's notice, and to stream live video across the globe instantaneously. Today, these conveniences are possible because the digital cameras and photo-editing systems used by photographers and artists, the compression and transmission systems used by distributors and network engineers, and the various multimedia and display technologies enjoyed by consumers all have the ability to process images in ways that were unthinkable just 20 years ago. But despite the innovation and rapid advances in technology and despite the prevalence of higher-definition and more immersive content, one thing has remained constant throughout the digital imaging revolution: the biological hardware used by consumers—the human visual system. Although personal preferences can and do change over time and can and do vary from person to person, the underlying neural circuitry and biological processing strategies have changed very little over measurable human history. As a result, digital
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