Why is image quality assessment so difficult?

By Zhou Wang, A. Bovik, Ligang Lu. 2002

939 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

41 OBJECTIVE VIDEO QUALITY ASSESSMENT (2003, background): “In [8] and [68], a new philosophy in designing image and video quality metrics has been proposed:”
Image Quality Assessment Based on Gradient Similarity (2012, neutral): “It is, however, well known that MSE/PSNR does not always agree with the subjective viewing results, particularly when distortion is not additive in nature [41].”
Assessment of image fusion procedures using entropy, image quality, and multispectral classification (2008, methodology): “[25] Z.”
Salient object detection: A survey (2014, background): “Some topics that are closely or remotely related to visual saliency include: salient object detection [41], fixation prediction [33, 34], object importance [17, 157, 182], memorability [78], scene clutter [169], video interestingness [50, 69, 90, 96], surprise [80], image quality assessment [208, 20”
Modern Image Quality Assessment (2006, background): “13a) [53, 54] was a catalyst that showed that the image quality assessment problem is, perhaps, not as difficult as had been thought for decades, and that it is possible to design easy-to-use and accurate image quality measures that are applicable to a wide variety of image distortion types.”
Blind image quality assessment through anisotropy. (2007, background): “Quality valuation of images is still an open and challenging probem [1].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “However, neither model consider the features of the HVS and viewing conditions, and are poorly correlated to subjective results [75]–[77].”
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”
Image Quality Assessment Using the SSIM and the Just Noticeable Difference Paradigm (2013, methodology): “Regarding bottom-up visual perception, the HVS processes visual information by analyzing structures, which is why techniques such as SSIM are so apt at predicting visual perception of distorted images [15].”
A modified PSNR metric based on HVS for quality assessment of color images (2011, background): “The scope of conventional HVS based evaluation model is limited, as they consider error sensitivity as the only parameter for assessment of image quality [2].”
Deep Learning for Image Super-Resolution: A Survey (2019, background): “Considering that the human visual system (HVS) is highly adapted to extract structural information from the viewing ﬁeld [63], the structural similarity index (SSIM) [62] is proposed for measuring the structural similarity between images, based on three relatively independent comparisons, namely…”
CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models (2019, methodology): “Hence, we use SSIM (Structural Similarity Index Measure) [17], which has been shown to be a better measure of what humans consider to be similar images [16].”
Video quality assessment based on structural distortion measurement (2004, background): “In [4], [7], a new philosophy in designing image and video quality metrics was introduced:”
Image quality assessment: from error visibility to structural similarity (2004, positive): “In [6] and [9], a new framework for the design of image quality measures was proposed, based on the assumption”
No-reference perceptual quality assessment of JPEG compressed images (2002, background): “a great deal of effort has been made to develop new objective image/video quality metrics that incorporate perceptual quality measures by considering Human Visual System (HVS) characteristics [1]–[4].”
Image Quality Assessment: From Error Measurement to Structural Similarity (2004, background): “In [6] and [9], a new framework for the design of image quality measures was proposed, based on the assumption that the human visual system is highly adapted to extract structural information from the viewing field.”