Journal ArticleDOI
Automatic Prediction of Perceptual Image and Video Quality
Alan C. Bovik
- Vol. 101, Iss: 9, pp 2008-2024
TLDR
The principles and methods of modern algorithms for automatically predicting the quality of visual signals are discussed and divided into understandable modeling subproblems by casting the problem as analogous to assessing the efficacy of a visual communication system.Abstract:
Finding ways to monitor and control the perceptual quality of digital visual media has become a pressing concern as the volume being transported and viewed continues to increase exponentially. This paper discusses the principles and methods of modern algorithms for automatically predicting the quality of visual signals. By casting the problem as analogous to assessing the efficacy of a visual communication system, it is possible to divide the quality assessment problem into understandable modeling subproblems. Along the way, we will visit models of natural images and videos, of visual perception, and a broad spectrum of applications.read more
Citations
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Journal ArticleDOI
Using free energy principle for blind image quality assessment
TL;DR: A new no-reference (NR) image quality assessment (IQA) metric is proposed using the recently revealed free-energy-based brain theory and classical human visual system (HVS)-inspired features to predict an image that the HVS perceives from a distorted image based on the free energy theory.
Journal ArticleDOI
Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.
TL;DR: This work proposes a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian response.
Journal ArticleDOI
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging
TL;DR: The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, and without estimating a depth-dependent transmission map.
Journal ArticleDOI
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
TL;DR: A deep neural network-based approach to image quality assessment (IQA) that allows for joint learning of local quality and local weights in an unified framework and shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
Journal ArticleDOI
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Deepti Ghadiyaram,Alan C. Bovik +1 more
TL;DR: The LIVE In the Wild Image Quality Challenge Database is designed and created, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices, and a new online crowdsourcing system is implemented, which is used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
The Fractal Geometry of Nature
TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.
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Proceedings ArticleDOI
Multiscale structural similarity for image quality assessment
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Journal ArticleDOI
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.