No-reference perceptual quality assessment of JPEG compressed images

By Hamid R. Sheikh, Zhou Wang, A. Bovik. Proceedings. International Conference on Image Processing, 2002

1,020 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7837dd8136789f061b8a1e74353d4c93a08b2a8d

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Citations in the corpus

Using Free Energy Principle For Blind Image Quality Assessment (2015, methodology): “To solve the problem of the dependence of original images, many blind quality measures have been developed for specific distortion types during the last decade [21]–[28].”
Unsupervised feature learning framework for no-reference image quality assessment (2012, background): “The goal is to build a computational model to predict human perceived image quality, accurately and automatically without access to reference images [1, 11, 13, 14, 17, 18, 22, 23, 26].”
Photo and Video Quality Evaluation: Focusing on the Subject (2008, background): “There have been a number of works on image quality assessment concerning image degradation caused by noise, distortion, and compression artifacts [1], [2], [3].”
> Replace This Line with Your Paper Identification Number (double-click Here to Edit) < (methodology): “As examples, we perform some experiments by using the metrics proposed in [8][9] to compute the visual quality.”
Digital Video Quality: Vision Models and Metrics (2005, background): “Several such metrics have been developed for blockiness (Winkler et al., 2001; Wang et al., 2002), blur (Marziliano et al.”
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild (2020, methodology): “Early attempts at BIQA were tailored to specific synthetic distortions, such as JPEG compression [18] and JPEG2000 compression [19].”
Human-Visual-System-Inspired Underwater Image Quality Measures (2016, result): “It is seen that the UIQM has the greatest SRCC, PRCC, and KRCC values compared to CIQI [13], CRME [40], and JPQM [48].”
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, methodology): “…those that use a hermite transform-based approach to model blurred edges [13], those that estimate ﬁrst-order differences and activity in an image [17], those that utilize an importance map weighting of spatial blocking scores [18], those that use a threshold-based approach on computed gradients…”
Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition (2014, methodology): “…measures is somewhat controversial [45], [46], up to date, these are probably the most widely used methods for IQA as they conveniently make use of many known psychophysical features of the human visual system [47], they are easy to calculate and usually have very low computational complexity.”
Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation (2009, background): “…the IQA task becomes very difficult, and almost all existing no-reference IQA metrics were designed for one or a set of predefined specific distortion types (such as blocking [2]–[5] and blurring [5] in JPEG; and ringing [6], blurring [6] and wavelet quantization effect [7], [8] in JPEG2000).”
Blind Image Quality Assessment Using a General Regression Neural Network (2011, methodology): “Blind JPEG QA was considered in [ 22 ] using a spatial activity measure.”
Blind image quality evaluation using perception based features (2015, methodology): “The algorithms in [1], [2], [3], [4], [5] require prior information about the type of distortion (like JPEG, JPEG2000, Blur, ringing, etc.”
Blind Image Quality Assessment via Deep Learning (2015, background): “[23] proposed a computational and memory efficient quality assessment model for JPEG images to deal with blocking artifacts.”
Quality-aware images (2006, methodology): “(NR) [7], and the JPEG2000 quality assessment method by Sheikh et al.”
Modern Image Quality Assessment (2006, methodology): “The interblock blurring and blocking artifacts created by block-based image compression can be explained either in the spatial domain [80–85] or in the frequency domain [86–88].”
Two-Level Approach for No-Reference Consumer Video Quality Assessment (2019, background): “blur or noise or JPEG compression artifacts [34]–[36].”
A Two-Step Framework for Constructing Blind Image Quality Indices (2010, methodology): “For example, there exist NR IQA algorithms that seek to assess the quality of JPEG/ JPEG2000 compressed images [1], [2] or blurred images [3].”