Blind image quality metric for blurry and noisy image

Seghir, Zianou Ahmed; Hachouf, Fella; Morain-Nicolier, Frédéric

doi:10.1109/iciip.2013.6707581

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The characteristic values that are obtained via an image evaluation algorithm can be used to repair the image quality degradation that is caused by defocused equipment. The result of the image evaluation algorithm should be consistent with the subjective perception of the human eyes [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Zhang

Lin

2023

Sensors

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Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

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Section: Introductionmentioning

confidence: 99%

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Zhang

Lin

2023

Sensors

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“…The perfection version of image is used to compare the distorted one in FR method [41][42][43][44]. NR [39][40] do not need a reference image and has only access to image test, its quality is evaluated without knowing the ideal version. RR methods assess the quality of test image using some features of reference image and the entire test one.…”

Section: Introductionmentioning

confidence: 99%

Color distortion and edge feature for perceptual quality assessment

Zeggari

Seghir

Hemam

et al. 2022

IJCAI

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The color distortion effect has an important impact on the perceived quality, which is ignored in previous related works. Unified with the color distortion outcome and edge similarity, a new fullreference image quality assessment was proposed named the gradient similarity-based distorted pixel and deformed color measure (GDCM). The components RGB of the color image are converted into image coded in YIQ color space. Then, Ruderman operators and the gradient images are calculated from the Y component. I and Q elements are used to identify the color distortion. Finally, the previous results are combined to compute the ultimate measure. Experimental results on databases illustrate that the GDCM performs very well. Povzetek: Prispevek predstavlja novo tehniko za oceno kakovosti slike, ki temelji na barvnem popačenju in značilnostih robov.

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Blind image quality metric for blurry and noisy image

Cited by 2 publications

References 11 publications

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Color distortion and edge feature for perceptual quality assessment

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