Modern Image Quality Assessment

By Zhou Wang, A. Bovik. Modern Image Quality Assessment, 2006

1,176 citations recorded by the source corpus.

This book is about objective image quality assessmentwhere the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/483950ef10a25aa1a2b0e2bac4292a410b28e4d5

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Citations in the corpus

Objective Quality Assessment of Tone-Mapped Images (2013, background): “Typical objective image quality assessment (IQA) approaches assume the reference and test images to have the same dynamic range [12], and thus cannot be directly applied to evaluate tone mapped images.”
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, methodology): “This observation is founded on SSIM [36] and CIEDE2000 [37] image quality metrics, computed using the known reference and the dehazed results produced by different dehazing techniques.”
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild (2020, methodology): “W ITH the inelastic demand for processing massive Internet images, it is of paramount importance to develop computational image quality models to monitor, maintain, and enhance the perceived quality of the output images of various image processing systems [1].”
Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods (2010, background): “In the last years many different methods for image quality assessment have been proposed and analyzed with the goal of developing a quality metric that correlates with perceived quality measurements (for a detailed review see [36]).”
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, background): “Others have proposed metrics that try to exploit known aspects of the human visual system (HVS) such as contrast sensitivity [22], high-level structural acuity [52], and masking [48, 51], or use other statistics/features [3,4,14,15,38,44–46,53,54,57].”
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, background): “Objective quality assessment can be divided into three categories depending on the amount of information provided to the algorithm [ 1 ].”
Robust Compressed Sensing MRI with Deep Generative Priors (2021, methodology): “Hence, we also report the masked SSIM and PSNR values in Figures 6 and 7.”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, methodology): “From these tables, we can conclude that in terms of structure and color restoration the methods of Meng et al. [26], Berman et al. [7], Ancuti et al. [3] and Ren et al. [32] perform the best in average when considering the SSIM, PSNR and CIEDE indices.”
F Mean Squared Error: Love It or Leave It? (2009, background): “This makes sense for a couple of reasons: first, images, like other natural signals, contain structures that occur over a range of spatial scales, and second, the human visual system decomposes visual data into multiscale spatial channels early in the visual pathway [3].”
Group MAD Competition? A New Methodology to Compare Objective Image Quality Models (2016, background): “Any transformation may introduce distortions that result in degradations in visual quality [24, 30].”
An Anti-Phishing System Employing Diffused Information (2014, methodology): “Perhaps the most commonly used intensity-based index is the mean square error [Daly 1993; Lubin 1993; Pappas and Safranek 2000; Wang and Bovik 2006].”
Image Quality Assessment: Unifying Structure and Texture Similarity (2020, background): “aracterized by restricted visual stimuli), leading to a number of problems regarding the deﬁnition of visual quality, quantiﬁcation of suprathreshold distortions, and generalization to natural images [22]. The SSIM index [2] introduced the concept of comparing structure similarity (instead of measur”
D-HAZY: A dataset to evaluate quantitatively dehazing algorithms (2016, methodology): “Table I presents a detailed validation for the 23 Middleburry dataset images and table II shows the average values of SSIM and CIEDE2000 measured over the 1449 NYU-Depth images.”