An improved perception-based no-reference objective image sharpness metric using iterative edge refinement

Varadarajan, Srenivas; Karam, Lina J.

doi:10.1109/icip.2008.4711776

Cited by 47 publications

(20 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of them are based on pixel derivatives [1], kurtosis [2], and DCT coefficients [3], and a large group of metrics analyze the spread of the edges in an image [4], [5]. Although these metrics are often applied to assess the image quality, most neglect the effects from other possible degradation sources, such as random noise.…”

Section: Introductionmentioning

confidence: 99%

A no-reference sharpness metric sensitive to blur and noise

Zhu

Milanfar

2009

2009 International Workshop on Quality of Multimedia Experience

115

View full text Add to dashboard Cite

A no-reference objective sharpness metric detecting both blur and noise is proposed in this paper. This metric is based on the local gradients of the image and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to noise ratio of the image. Experiments using synthetic, natural, and compressed images are presented to demonstrate the effectiveness and robustness of this metric. Its statistical properties are also provided.

show abstract

Section: Introductionmentioning

confidence: 99%

A no-reference sharpness metric sensitive to blur and noise

Zhu

Milanfar

2009

2009 International Workshop on Quality of Multimedia Experience

115

View full text Add to dashboard Cite

show abstract

“…Early no-reference IQA (NR-IQA) models commonly operated under the assumption that the image quality is affected by one or several particular kinds of distortions, such as blockiness [1], [2], ringing [3], blur [4], [5], or compression [6]- [9]. Such early NR-IQA approaches therefore extract distortion-specific features for quality prediction, based on a model of the presumed distortion type(s).…”

mentioning

confidence: 99%

A Feature-Enriched Completely Blind Image Quality Evaluator

Zhang

Bovik

2015

IEEE Trans. on Image Process.

981

416

View full text Add to dashboard Cite

Abstract-Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinionunaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed 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 BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk/∼cslzhang/IQA/ILNIQE/ILNIQE.htm.Index Terms-Blind image quality assessment, natural image statistics, multivariate Gaussian.

show abstract

“…Given that blur estimation methods most often work on the idea of measurement of edge-spread and blur manifests itself in smooth or diminished edges, some edges may remain undetected. Varadarajan et al [34] improved the method proposed in [29] by incorporating an edge refinement method to enhance the edge detection and hence outperformed the blur assessment. The authors achieved as much as 9% increase in Pearson's correlation coefficient.…”

Section: Blurringmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

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...

show abstract

An improved perception-based no-reference objective image sharpness metric using iterative edge refinement

Cited by 47 publications

References 9 publications

A no-reference sharpness metric sensitive to blur and noise

A no-reference sharpness metric sensitive to blur and noise

A Feature-Enriched Completely Blind Image Quality Evaluator

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

Contact Info

Product

Resources

About