A Two-Step Framework for Constructing Blind Image Quality Indices

Moorthy, Anush K.; Bovik, Alan C.

doi:10.1109/lsp.2010.2043888

Cited by 1,108 publications

(562 citation statements)

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“…Recently, a two-stage model was proposed for designing distortion-blind no-reference image quality assessment (IQA) algorithms that first seeks to identify the distortion(s) that are present in the image, then proceeds to perform distortion-specific quality assessment [13]. A combination of the two stages leads to a generalpurpose blind IQA model.…”

Section: Distortion-identification and Quality Assessmentmentioning

confidence: 99%

“…This procedure is described in great detail in [15,13]. Supposing n distortion types (in GENII-1, n ¼4), n regression modules (support vector regression (SVRs) [56]) are trained, taking as input DIIVINE or BRISQUE features, and then regressed onto (known) quality scores for each of the distortion types independently.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…In order to achieve these goals, we use realizations of a previously proposed two-stage framework for image quality assessment [13] that first identifies the distortion that afflicts the image [14] and then proceeds to assess quality. Two realizations of this two-step framework have been developed -one in the wavelet domain [15] and the other in the spatial domain [16] -both capable of accurate distortion-identification as well as blind parameter estimation and blind image quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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Perceptually optimized blind repair of natural images

Moorthy

Mittal

Bovik

2013

Signal Processing: Image Communication

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. b s t r a c tWe define the new idea of blind image repair as a process of correcting one or more different and unknown types of distortions afflicting an image. These distortions could introduce linear or non-linear degradations, compression artifacts, noise, etc., or combinations of these. Thus the concept encompasses denoising, deblurring, deblocking, deringing, and other post-acquisition image improvement processes that address distortions. The problem is distortion-blind when the natures of the distortion processes are unknown prior to analyzing the image. Towards solving this problem, we describe a new framework for repairing an image that has undergone an unknown set of distortions, based on identifying the distortion(s) present in the image (if any) and applying possibly multiple distortion-specific image repair algorithms. Our philosophy is based on the principle that the task of general purpose image repair is one of agglomeration, i.e., the algorithm should embody multiple high-performing distortion-specific repair modules such that seamless general purpose image repair is achieved. Our proposed frameworkthe GEneral-purpose No-reference Image Improver (GENII) -enables the design of algorithms that are blind to distortion type as well as to distortion parameters, and only requires as input the distorted image to be repaired. The GENII framework is modular and easily extensible to image repair problems beyond those considered here. GENII operates by using natural scene statistic models to identify distortion, to perceptually optimize the distortion parameter(s), to assess the quality of the intermediate repaired images, and to perceptually optimize the repair processes. We explain the general purpose image repair framework and one specific realization, dubbed GENII-1, which assumes that the image has been affected by one or more of four possible distortion types.The performance of GENII-1 is evaluated on 4000 distorted images, and shown to deliver substantial improvements in both quantitative and qualitative visual quality.

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Section: Distortion-identification and Quality Assessmentmentioning

confidence: 99%

Section: Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Perceptually optimized blind repair of natural images

Moorthy

Mittal

Bovik

2013

Signal Processing: Image Communication

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“…A two-stages framework, is proposed by Moorthy and Bovik [1], which first classifies the distortion using a classification model then predict the quality value using regression models. In this paper, we improve the regression portion by using different set of features for each distortion instead of using same features for all distortions.…”

Section: Introductionmentioning

confidence: 99%

No-reference image quality metric based on features evaluation

Alaql

Ghazinour

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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Abstract-We develop an efficient general-purpose noreference (NR) image quality assessment (IQA) model that based on a hypothesis that an effective combination of image features can be used to develop NR-IQA approaches having competitive performance with the state-of-the-art. First, we design a collection of features, then evaluate the usefulness of each feature on different kinds of distortions using different features evaluation techniques. Therefore, we came up with a set of optimal features for each learning model in the framework. Our experimental results show that our approach outperforms state-of-the-art blind image quality prediction models.

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“…In training-based approaches, one important issue is how to design proper features, while one feature may be only sensitive to one kind of distortion. In [8], Moorthy et al proposed a two-stage framework, which is a combination of NSS-based approaches and trainingbased approaches, using support vector machine (SVM) to classify an image into a distortion class and then using a distortion specific quality metric to predict its quality. In [9,10], Peng et al proposed a training-based approach, namely CBIQ, using Gabor filter to extract local features and visual codebook to quantize the feature space.…”

Section: Introductionmentioning

confidence: 99%

Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering

Zhang

2013

2013 IEEE International Conference on Image Processing

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The goal of no-reference image quality assessment (NR-IQA) is to estimate the quality of an image consistent with the human perception of the image automatically without any prior of the reference image. In this paper, we present a simple yet efficient and effective approach to learn a blind Image Quality index based on Visual saliency guided sampling and Gabor filtering, namely IQVG. Given an image, we at first randomly sample a sufficient number of image patches guided by the image's visual saliency map and convolve each patch with Gabor filters to get a bag of features. Then, the image is represented by using a histogram to encode the bag of features. Support vector regression (SVR) is used to learn the mapping from feature space to image quality. Extensive experiments conducted on the LIVE IQA database demonstrate the overall superiority of our IQVG over the other state-of-the-art NR-IQA algorithms evaluated. The Matlab source code of IQVG and the evaluation results are available online at

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A Two-Step Framework for Constructing Blind Image Quality Indices

Cited by 1,108 publications

References 10 publications

Perceptually optimized blind repair of natural images

Perceptually optimized blind repair of natural images

No-reference image quality metric based on features evaluation

Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering

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