Zhengfang Duanmu, Wentao Liu, Zhongling Wang, Zhou Wang
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Quantifying Visual Image Quality: A Bayesian View.
Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their quality as perceived by human observers. IQA modeling plays a special bridging role between vision science and engineering practice, both as a test-bed for vision theories and computational biovision models and as a powerful tool that could potentially have a profound impact on a broad range of image processing, computer vision, and computer graphics applications for design, optimization, and evaluation purposes. The growth of IQA research has accelerated over the past two decades. In this review, we present an overview of IQA methods from a Bayesian perspective, with the goals of unifying a wide spectrum of IQA approaches under a common framework and providing useful references to fundamental concepts accessible to vision scientists and image processing practitioners. We discuss the implications of the successes and limitations of modern IQA methods for biological vision and the prospect for vision science to inform the design of future artificial vision systems. (The detailed model taxonomy can be found at http://ivc.uwaterloo.ca/research/bayesianIQA/.).
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.