A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

Ferzli, Rony; Karam, Lina J.

doi:10.1109/tip.2008.2011760

Cited by 691 publications

(418 citation statements)

References 17 publications

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“…Based on the IQ metrics described in Section II, we choose two FR metrics [63] (SSIM [21], VIF [22]) and four NR metrics (JNBM [39], LPC-SI [48], BRISQUE [52], [53], NIQE [55], [56]) to predict IQ. These metrics can be considered to be state of the art, and has shown to perform well in existing evaluation studies.…”

Section: Methodsmentioning

confidence: 99%

“…There is an important notion proposed by Ferzli and Karam: Just-Noticeable Blur (JNB) [37], [38], which takes into account the response of the HVS to sharpness at different contrast levels. The derived HVS-based sharpness perception model is used to predict the relative perceived sharpness in images with different content [39]. Later more and more research is based on the JNB [40], [41], [42], [43], [44].…”

Section: ) Full Reference Metricsmentioning

confidence: 99%

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Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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Abstract-Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.

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Section: Methodsmentioning

confidence: 99%

Section: ) Full Reference Metricsmentioning

confidence: 99%

Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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show abstract

“…brightness and color) and affects some structural image distortions (e.g. sharpness and blocking artifacts) [7] [8]. Hence, the contrast measure is very important in image quality assessment and image enhancement.…”

Section: Contrast/image Dynamic/brightnessmentioning

confidence: 99%

Automated Visual Quality Analysis for Media Production

Fassold

Wechtitsch

Hofmann

et al. 2012

2012 IEEE International Symposium on Multimedia

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Abstract-Automatic quality control for audiovisual media is an important tool in the media production process. In this paper we present tools for assessing the quality of audiovisual content in order to decide about the reusability of archive content. We first discuss automatic detectors for the common impairments noise and grain, video breakups, sharpness, image dynamics and blocking. For the efficient viewing and verification of the automatic results by an operator, three approaches for user interfaces are presented. Finally, we discuss the integration of the tools into a service oriented architecture, focusing on the recent standardization efforts by EBU and AMWA's Joint Task Force on a Framework for Interoperability of Media Services in TV Production (FIMS) .

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“…Therefore, this letter aims to propose such a metric and the novelty of our metric lies in its effectiveness in measuring the energy with the purpose of accurately estimating the blur strength. Also, to our knowledge, such a metric that attempts to analyze the whole FT-CDF itself is not found in the existing NR-F metrics [7].…”

Section: Introductionmentioning

confidence: 99%

No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images

Park

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.

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A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

Cited by 691 publications

References 17 publications

Evaluation of image quality metrics for sharpness enhancement

Evaluation of image quality metrics for sharpness enhancement

Automated Visual Quality Analysis for Media Production

No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images

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