Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Image quality assessment that aims to evaluate the image quality automatically by a computational model plays a significant role in image processing systems. To meet the need of accuracy and effectiveness, in the proposed method, complementary features including histogram of oriented gradient, edge information, and color information are employed for joint representation of the image quality. Afterwards, the dissimilarities of the extracted features between the distorted and reference images are quantified. Finally, support vector regression is used for distortion indices fusion and objective quality mapping. Experimental results validate that the proposed method outperforms the state-of-the-art methods in terms of consistency with subjective perception and robustness across various databases and different distortion types.

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No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Journal of Imaging ◽

10.3390/jimaging6080075 ◽

2020 ◽

Vol 6 (8) ◽

pp. 75

Author(s):

Domonkos Varga

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Reference Image ◽

Phase Congruency ◽

Perceptual Features ◽

Proposed Model ◽

Dark Channel ◽

Reference Images ◽

Spatial Domains

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

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Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

Applied Sciences ◽

10.3390/app9122499 ◽

2019 ◽

Vol 9 (12) ◽

pp. 2499

Author(s):

Yiling Tang ◽

Shunliang Jiang ◽

Shaoping Xu ◽

Tingyun Liu ◽

Chongxi Li

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Color Space ◽

Support Vector ◽

Feature Maps ◽

Hsv Color Space ◽

Window Method ◽

Blind Image Quality Assessment ◽

Blind Image

To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.

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A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691308002501 ◽

2008 ◽

Vol 06 (04) ◽

pp. 541-551 ◽

Cited By ~ 16

Author(s):

WEN LU ◽

XINBO GAO ◽

DACHENG TAO ◽

XUELONG LI

Keyword(s):

Image Processing ◽

Computational Complexity ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Assessment Method ◽

Reference Image ◽

Subjective Perception ◽

Good Accordance ◽

Quality Assessment Method

Image quality is a key characteristic in image processing,10,11 image retrieval,12,13 and biometrics.14 In this paper, a novel reduced-reference image quality assessment method is proposed based on wavelet transform. By simulating the human visual system, we take the variance of the visual sensitive coefficients into account to measure a distorted image. The computational complexity of the proposed method is much lower compared with some existing methods. Experimental results demonstrate its advantages in terms of correlation coefficient, outlier ratio, transmitted information, and CPU cost. Moreover, it is also illustrated that the proposed method has a good accordance with human subjective perception.

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Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

Journal of Engineering ◽

10.1155/2017/4752378 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Ruizhe Deng ◽

Yang Zhao ◽

Yong Ding

Keyword(s):

Feature Extraction ◽

Visual Cortex ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Subjective Evaluation ◽

Visual Saliency ◽

Perceptual Quality ◽

Subjective Rating ◽

Support Vector

Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases.

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Image quality assessment based on multi-feature extraction and synthesis with support vector regression

Signal Processing Image Communication ◽

10.1016/j.image.2017.03.001 ◽

2017 ◽

Vol 54 ◽

pp. 81-92 ◽

Cited By ~ 10

Author(s):

Yong Ding ◽

Yang Zhao ◽

Xinyu Zhao

Keyword(s):

Feature Extraction ◽

Image Quality ◽

Quality Assessment ◽

Support Vector Regression ◽

Image Quality Assessment ◽

Support Vector

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Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment

Sensors ◽

10.3390/s21041043 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1043

Author(s):

Igor Stępień ◽

Rafał Obuchowicz ◽

Adam Piórkowski ◽

Mariusz Oszust

Keyword(s):

Magnetic Resonance ◽

Image Quality ◽

Quality Assessment ◽

Magnetic Resonance Image ◽

Image Quality Assessment ◽

Magnetic Resonance Images ◽

Subsequent Treatment ◽

Support Vector ◽

Network Architectures ◽

Deep Convolutional Neural Networks

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.

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Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Entropy ◽

10.3390/e23111525 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1525

Author(s):

Krzysztof Okarma ◽

Wojciech Chlewicki ◽

Mateusz Kopytek ◽

Beata Marciniak ◽

Vladimir Lukin

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Subjective Evaluation ◽

General Purpose ◽

Sensing Applications ◽

Objective Quality ◽

Panoramic Images ◽

Remote Sensing Applications

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

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Edge Detection technique based on HDR image quality assessment

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012029 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012029

Author(s):

Dingxian Wang

Keyword(s):

Image Quality ◽

Edge Detection ◽

Quality Assessment ◽

Image Quality Assessment ◽

Dynamic Range ◽

High Dynamic Range ◽

Edge Information ◽

Image Edge Detection ◽

Image Edge ◽

Hdr Image

Abstract Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.

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Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead

London Imaging Meeting ◽

10.2352/issn.2694-118x.2021.lim-1 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 1-4

Author(s):

Seyed Ali Amirshahi

Keyword(s):

Image Processing ◽

Computer Vision ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Machine Learning Techniques ◽

Short Paper ◽

Subjective Quality Assessment ◽

Learning Techniques ◽

Objective Quality

Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective quality metrics have been the go to approach. Until recently most such metrics have taken advantage of different handcrafted features. Similar (but with a slower speed) to other applications in image processing and computer vision, different machine learning techniques, more specifically Convolutional Neural Networks (CNNs) have been introduced in different tasks related to image quality assessment. In this short paper which is a supplement to a focal talk given with the same title at the London Imaging Meeting (LIM) 2021 we aim to provide a short timeline on how CNNs have been used in the field of image quality assessment so far, how the field could take advantage of CNNs to evaluate the image quality, and what we expect will happen in the near future.

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