Reduced- and No-Reference Image Quality Assessment

Wang, Zhou; Bovik, A.C.

doi:10.1109/msp.2011.942471

Cited by 259 publications

(109 citation statements)

References 56 publications

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“…The signs of the transformed image coefficients (1) have been observed to follow a fairly regular structure. However, distortions disturb this correlation structure [2].…”

Section: Brisque Featuresmentioning

confidence: 94%

“…Various quantitative measures of image quality have been proposed under which different amounts of a priori information is assumed to be available [1]. Generally, no reference or blind IQA models seek to predict the quality of distorted images using learned natural and distorted scene knowledge but without access to reference images [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Making image quality assessment robust

Mittal

Moorthy

Bovik

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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We develop a robust framework for natural scene statistic (NSS) model based blind image quality assessment (IQA). The robustified IQA model utilizes a robust statistics approach based on L-moments. Such robust statistics based approaches are effective when natural or distorted images deviate from assumed statistical models, and achieves better prediction performance on distorted images relative to human subjective judgments. We also show how robustifying the model makes IQA approach resilient against deviation in model assumptions, small variations in the distortions and amount of data the model is trained on.

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“…The signs of the transformed image coefficients (1) have been observed to follow a fairly regular structure. However, distortions disturb this correlation structure [2].…”

Section: Brisque Featuresmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Making image quality assessment robust

Mittal

Moorthy

Bovik

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

View full text Add to dashboard Cite

show abstract

“…The feature-based methods are based on either a model developed for particular artifacts related to a visible degradation, or a model developed to quantify the impact of degradations on a specific set of attributes of http://jivp.eurasipjournals.com/content/2014/1/40 the original uncorrupted image or video. A brief survey of NR methods of image quality assessment (IQA) based on the notion of quantifying the impact of distortions on natural scene statistics (NSS) is provided in [5]. Some NR methods of visual quality are discussed in [6] also under the categorization of features and artifacts detection.…”

Section: Related Work: Published Reviews Of Objective Visual Quality mentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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“…13 By quantifying the 'unnaturalness' caused by a distortion process, these models have been successful ingredients of algorithm that can accurately predict human judgements of visual quality 11 .…”

Section: Proposed Approachmentioning

confidence: 99%

Blind image quality assessment without training on human opinion scores

et al. 2013

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We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These 'completely blind' models compete well with standard non-blind image quality indices in terms of subjective predictive performance when tested on the large publicly available 'LIVE' Image Quality database.

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Reduced- and No-Reference Image Quality Assessment

Cited by 259 publications

References 56 publications

Making image quality assessment robust

Making image quality assessment robust

No-reference image and video quality assessment: a classification and review of recent approaches

Blind image quality assessment without training on human opinion scores

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