Perceptual criteria for image quality evaluation

By Thrasyvoulos N. Pappas. 2005

341 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e2f9f89f1c30a9f1f0d9493601f5803ce33b02ef

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Learning Universal Adversarial Perturbations with Generative Models (2017, methodology): “Since we expect a high degree of structure in a UAP, SSIM is measured in addition to MSE, as it has been argued that MSE does not map well to a human’s perception of image structure [26], [37].”
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections (2020, methodology): “As measuring perceptual image similarity is challenging [26, 38, 40, 42], we present rendered images for visual inspection and report quantitative results based on PSNR, MS-SSIM [41], and LPIPS [42].”
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: from error visibility to structural similarity (2004, neutral): “Reviews on image and video quality assessment algorithms can be found in [4], [11]–[13].”
Image information and visual quality (2006, methodology): “A detailed discussion of these methods can be found in [4]–[6].”
Quality-aware images (2006, background): “On the other hand, most objective image/video quality assessment methods proposed in the literature [1]–[3] are not applicable in this scenario because they are full-reference (FR) methods that require access to the original images as references.”
Video Quality Assessment Using a Statistical Model of Human Visual Speed Perception (2007, neutral): “Third, many visual experiments that measure temporal visual sensitivities were done with flickering patterns [1], which do not reflect any physical motion of the objects.”
41 OBJECTIVE VIDEO QUALITY ASSESSMENT (2003, background): “A more detailed review of image quality assessment metrics may be found in [2,36].”
An information fidelity criterion for image quality assessment using natural scene statistics (2005, methodology): “A detailed discussion of these methods can be found in [3]–[5].”
Perceptual visual quality metrics: A survey (2011, background): “In spite of the recent progress in related fields, objective evaluation of picture quality in line with human perception is still a long and difficult odyssey [38,123,156,157,163,183] due to the complex, multi-disciplinary nature of the problem (related to physiology, psychology, vision research and”
Information Content Weighting for Perceptual Image Quality Assessment (2011, background): “I N RECENT years, there has been an increasing interest in developing objective image quality assessment (IQA) methods that can automatically predict human behaviors in evaluating image quality [1]–[3].”
Image Quality Assessment: From Error Measurement to Structural Similarity (2004, background): “Reviews on image and video quality assessment algorithms can be found in [4], [11]–[13].”
F Mean Squared Error: Love It or Leave It? (2009, background): “For tutorial reviews and more detailed descriptions about such methods, refer to [2]–[4].”
A new automated quality assessment algorithm for image fusion (2009, methodology): “A vision model takes advantage of the lower level physiology of the visual system to determine the visual sensitivity [13].”