Journal ArticleDOI
Blind Image Quality Assessment Using a General Regression Neural Network
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TLDR
A no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN) trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types.Abstract:
We develop a no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN). The new algorithm is trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types. The features deployed for QA include the mean value of phase congruency image, the entropy of phase congruency image, the entropy of the distorted image, and the gradient of the distorted image. Image quality estimation is accomplished by approximating the functional relationship between these features and subjective mean opinion scores using a GRNN. Our experimental results show that the new method accords closely with human subjective judgment.read more
Citations
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Proceedings ArticleDOI
Convolutional Neural Networks for No-Reference Image Quality Assessment
TL;DR: A Convolutional Neural Network is described to accurately predict image quality without a reference image to achieve state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments.
Journal ArticleDOI
A Feature-Enriched Completely Blind Image Quality Evaluator
TL;DR: The proposed opinion-unaware BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIZA methods.
Proceedings ArticleDOI
Unsupervised feature learning framework for no-reference image quality assessment
TL;DR: This paper uses raw image patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner and uses soft-assignment coding with max pooling to obtain effective image representations for quality estimation.
Journal ArticleDOI
Seven Challenges in Image Quality Assessment: Past, Present, and Future Research
TL;DR: An up-to-date review of research in IQA is provided, and several open challenges in this field are highlighted, including key properties of visual perception, image quality databases, existing full-reference, no- reference, and reduced-reference IQA algorithms.
Journal ArticleDOI
Fully Deep Blind Image Quality Predictor
Jongyoo Kim,Sanghoon Lee +1 more
TL;DR: A blind image evaluator based on a convolutional neural network (BIECON) is proposed that follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR- IQA prediction accuracy that is comparable with that of state-of-the-art FR-iqA methods.
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.
Journal Article
The mathematical theory of communication
Claude E. Shannon,Warren Weaver +1 more
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
Book
The Mathematical Theory of Communication
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
Journal ArticleDOI
A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
Proceedings ArticleDOI
Multiscale structural similarity for image quality assessment
TL;DR: This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions, and develops an image synthesis method to calibrate the parameters that define the relative importance of different scales.
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