A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling

Sadaka, Nabil G.; Karam, Lina J.; Ferzli, Rony; Abousleman, Glen P.

doi:10.1109/icip.2008.4711768

Cited by 69 publications

(24 citation statements)

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“…In [29], the block size used for finding edge pixels is 64 × 64, and a similar contribution based on JNB from the same authors is reported in [30] where a block size of 8 × 8 has been used for finding the edge pixels. The method proposed in [30] has been improved in [31] by adding the impact of saliencyweighting in foveated regions of an image. Specifically, more weighting is given to the local blur estimates that belong to salient regions of an image, while spatial blur values are pooled together to compute an overall value of blur for the whole image.…”

Section: Blurringmentioning

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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Section: Blurringmentioning

confidence: 99%

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

et al. 2014

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“…To this end, a variety of computational models of visual attention is implemented in different metrics by weighting local distortion maps with local saliency maps, a process referred to as "visual importance pooling" (see. e.g., [26] and [27]). The attention models used in these studies, however, are either specifically designed or chosen for a specific domain, and their accuracy in predicting human attention in general terms is not always fully proved yet.…”

Section: Added Value Of Visual Attention In Nr Blur Metricsmentioning

confidence: 99%

Issues in the design of a no-reference metric for perceived blur

Liu

Heynderickx

2011

SPIE Proceedings

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Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference (NR) type, which implies that quality prediction is based on the distorted image only. Recent progress in the development of a NR blur metric is evident from many promising methods reported in the literature. However, there is still room for improvement in the design of a NR metric that reliably predicts the extent to which humans perceive blur. In this paper, we address some important issues relevant to the design as well as the application of a NR blur metric. Its purpose is not to describe a particular metric, but rather to explain current concerns and difficulties in this field, and to outline how these issues may be accounted for in the design of future metrics.

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“…No-reference (NR) metrics estimate QoE though mainly measuring image distortions: blockiness (Leontaris & Reibman, 2005;Saad, Bovik & Charrier, 2010;Zhou, Bovik & Evan, 2000), blur (Marziliano, Dufaux, Winkler & Ebrahimi, 2002;Sadaka, Karam, Ferzli & Abousleman, 2008;Yun-Chung, Jung-Ming, Bailey, Sei-Wang & Shyang-Lih, 2004), and noise (Ghazal, Amer & Ghrayeb, 2007). An overview of existing NR image and video quality estimation studies have been given by Hemai and Reibman (Hemami & Reibman, 2010).…”

Section: Qoe Metricsmentioning

confidence: 99%

Understanding User Experience of Mobile Video: Framework, Measurement, and Optimization

Song¹,

Tjondronegoro²,

Docherty³

2012

Mobile Multimedia - User and Technology Perspectives

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A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling

Cited by 69 publications

References 7 publications

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

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

Issues in the design of a no-reference metric for perceived blur

Understanding User Experience of Mobile Video: Framework, Measurement, and Optimization

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