Histogram modelling-based no reference blur quality measure

Kerouh, Fatma; Ziou, Djemel; Serir, Amina

doi:10.1016/j.image.2017.08.014

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Another report measure the blurriness in an image from the steepness of probability density function. The probability density function models the histogram of discrete cosine transform coefficients of edge maps [ 21 ]. Color, edge, and structural information is the technique used to discriminate images with different levels of blur in [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

et al. 2018

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BackgroundRician noise, bias fields and blur are the common distortions that degrade MRI images during acquisition. Blur is unique in comparison to Rician noise and bias fields because it can be introduced into an image beyond the acquisition stage such as postacquisition processing and the manifestation of pathological conditions. Most current blur assessment algorithms are designed and validated on consumer electronics such as television, video and mobile appliances. The few algorithms dedicated to medical images either requires a reference image or incorporate manual approach. For these reasons it is difficult to compare quality measures from different images and images with different contents. Furthermore, they will not be suitable in environments where large volumes of images are processed. In this report we propose a new blind blur assessment method for different types of MRI images and for different applications including automated environments.MethodsTwo copies of the test image are generated. Edge map is extracted by separately convolving each copy of the test image with two parallel difference of Gaussian filters. At the start of the multiscale representation, the initial output of the filters are equal. In subsequent scales of the multiscale representation, each filter is tuned to different operating parameters over the same fixed range of Gaussian scales. The filters are termed low and high energy filters based on their characteristics to successively attenuate and highlight edges over the range of multiscale representation. Quality score is predicted from the distance between the normalized mean of the edge maps at the final output of the filters.ResultsThe proposed method was evaluated on cardiac and brain MRI images. Performance evaluation shows that the quality index has very good correlation with human perception and will be suitable for application in routine clinical practice and clinical research.

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Section: Introductionmentioning

confidence: 99%

“…In this report we propose a new approach to assess blur distortion in MRI images. The concept behind blur quality evaluation is the existence and persistence of edge information at different image resolutions [ 21 ]. Across increasing Gaussian scales, edges in higher quality images have higher persistence than lower quality images.…”

Section: Introductionmentioning

confidence: 99%

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

et al. 2018

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“…With the increase of Gaussian convolution scale, edges and details of the image are gradually blurred, resulting in the gradually attenuation of information and energy. Inspired by this, we proposed to evaluate the change of blur on scale images by the energy, which is defined as the sum of squares of the gradient: = ∑∑ (14) Considering that HVS tends to judge the sharpness of image by the sharpest region, while blur mainly affects the edges and texture areas of the image, with limited impact on smooth areas. We first calculate the energy of each block in the image, then sort these blocks in descending order according to the energy to take out the top p% high energy block, and calculate the average energy of these blocks reflecting sharp regions:…”

Section: Energy Ratio Features Of the Images Between Scalesmentioning

confidence: 99%

“…Sharpness score of the image is estimated by the sorted high frequency wavelet coefficients. Kerouh et al transformed the edge image into the frequency domain with DCT transformation [14], modeled the histogram of DCT coefficient with exponential probability density function (PDF), and the sharpness of image is measured by steepness of PDF. Evaluation methods in the frequency domain can obtain promising accuracy, but due to transformation from spatial domain to frequency domain, they usually require high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

Sun¹,

Cui²,

Gan³

et al. 2020

KSII TIIS

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Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

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“…The objective evaluation method mainly includes the Earth Mover Distance (EMD) [18], Edge Histogram (EH) [19] and the SIFT flow method (SF) [20]. EMD is a classic IRQA method, which was first proposed by Stolfi in 1994 to calculate the difference between the two images by using the earth mover distance of the two images.…”

Section: Image Quality Evaluation Technologymentioning

confidence: 99%

Content-Aware Retargeted Image Quality Assessment

Zhang

Guo

et al. 2019

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In targeting the low correlation between existing image scaling quality assessment methods and subjective awareness, a content-aware retargeted image quality assessment algorithm is proposed, which is based on the structural similarity index. In this paper, a similarity index, that is, a local structural similarity algorithm, which can measure different sizes of the same image is proposed. The Speed Up Robust Feature (SURF) algorithm is used to extract the local structural similarity and the image content loss degree. The significant area ratio is calculated by extracting the saliency region and the retargeted image quality assessment function is obtained by linear fusion. In the CUHK image database and the MIT RetargetMe database, compared with four representative assessment algorithms and other latest four kinds of retargeted image quality assessment algorithms, the experiment proves that the proposed algorithm has a higher correlation with Mean Opinion Score (MOS) values and corresponds with the result of human subjective assessment.

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Histogram modelling-based no reference blur quality measure

Cited by 10 publications

References 15 publications

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

Content-Aware Retargeted Image Quality Assessment

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