No-reference image quality assessment algorithms: A survey

Kamble, Vipin Milind; Bhurchandi, Kishor M.

doi:10.1016/j.ijleo.2015.02.093

Cited by 83 publications

(45 citation statements)

References 50 publications

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“…To evaluate the effect of the IR-EDOF, a tenengrad (Aslantas and Kurban, 2009) and entropy (Kamble and Bhurchandi, 2015) function for objective definition assessment metrics is adopted. The comparison of quality assessment results is shown in Table I.…”

Section: Sensor Reviewmentioning

confidence: 99%

A kind of infrared expand depth of field vision sensor in low-visibility road condition for safety-driving

Wang

Xiao-yan

et al. 2016

Sensor Review

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Purpose – This study aims to consider active vision in low-visibility environments to reveal the factors of optical properties which affect visibility and to explore a method of obtaining different depths of fields by multimode imaging.Bad weather affects the driver’s visual range tremendously and thus has a serious impact on transport safety. Design/methodology/approach – A new mechanism and a core algorithm for obtaining an excellent large field-depth image which can be used to aid safe driving is designed and implemented. In this mechanism, atmospheric extinction principle and field expansion system are researched as the basis, followed by image registration and fusion algorithm for the Infrared Extended Depth of Field (IR-EDOF) sensor. Findings – The experimental results show that the idea we propose can work well to expand the field depth in a low-visibility road environment as a new aided safety-driving sensor. Originality/value – The paper presents a new kind of active optical extension, as well as enhanced driving aids, which is an effective solution to the problem of weakening of visual ability. It is a practical engineering sensor scheme for safety driving in low-visibility road environments.

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Section: Sensor Reviewmentioning

confidence: 99%

A kind of infrared expand depth of field vision sensor in low-visibility road condition for safety-driving

Wang

Xiao-yan

et al. 2016

Sensor Review

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“…For several decades, the media industry and the image quality research community have worked on developing and deploying no-reference image quality evaluation tools [10]. This challenging problem requires developing prediction models that algorithmically map photos to scores representative of human judgments of perceived image quality.…”

Section: Introductionmentioning

confidence: 99%

Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality

et al. 2018

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The use of no-reference image quality evaluation tools that produce MOS scores, like the VIQET tool which was released by the Video Quality Expert Group, raises the question of whether the produced MOS differences between images correspond to noticeable differences in quality by the consumers. In this work, we attempted to approximate the minimum MOS difference that is required in order for people to be able to distinguish between a higher and a lower quality image under realistic conditions that are commonly encountered in the current consumer space. 91 people participated in a subjective justnoticeable-differences study across three countries that used non-simulated image stimuli, produced and evaluated through crowd sourcing for the validation of the VIQET no-reference image quality tool. The image dataset consisted of 15 different scenes belonging to three different scene types, with a total of 210 different image pairs being used. After evaluating the quality of the collected data, a logistic regression analysis approach was employed in order to estimate the minimum MOS difference required between two images in order for a given percentage of people to be able to detect the higher quality image.

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“…However, the original image is not available in real-world tasks. Therefore, No-Reference Image Quality Assessment (NR-IQA) technique [9,10] must be used to measure image quality. Our approach is focused on relationship between SNR and Hartley entropy.…”

Section: Introductionmentioning

confidence: 99%

Relationship between entropy and SNR changes in image enhancement

Krbcová

Kukal

2017

J Image Video Proc.

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There are many techniques of image enhancement. Their parameters are traditionally tuned by maximization of SNR criterion, which is unfortunately based on the knowledge of an ideal image. Our approach is based on Hartley entropy, its estimation, and differentiation. Resulting gradient of entropy is estimated without knowledge of ideal images, and it is a subject of minimization. Both SNR maximization and gradient magnitude minimization cause various settings of the given filter. The optimum settings are compared, and their differences are discussed.

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No-reference image quality assessment algorithms: A survey

Cited by 83 publications

References 50 publications

A kind of infrared expand depth of field vision sensor in low-visibility road condition for safety-driving

A kind of infrared expand depth of field vision sensor in low-visibility road condition for safety-driving

Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality

Relationship between entropy and SNR changes in image enhancement

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