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

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.

Future Directions in Image Quality

2019 ◽  
Vol 2019 (1) ◽  
pp. 399-403
Author(s):  
Seyed Ali Amirshahi ◽  
Marius Pedersen
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Short Paper ◽  
Future Directions ◽  
Perceptual Judgement ◽  
Made In

With the advancements made in the field of image processing and computer vision, the last few decades have seen an increase in studies focused on image quality assessment. While this has resulted in the introduction of different new metrics which some show high correlation with the perceptual judgement of the human observers there still exists a huge room for improvement. In this short paper which is prepared as a complement to the workshop on Future Directions in Image Quality at CIC 27 in Paris, France we aim to introduce future directions in the field and challenges facing ahead.

A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

2008 ◽  
Vol 06 (04) ◽  
pp. 541-551 ◽  
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.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

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

Entropy ◽  
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.

A Novel Method of Image Quality Assessment

2014 ◽  
Vol 556-562 ◽  
pp. 5064-5067 ◽  
Author(s):  
Ming Wei Guo ◽  
Chen Bin Zhang ◽  
Zong Hai Chen
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Selective Attention ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Visual Selective Attention ◽  
Research Areas ◽  
Novel Method

Image quality assessment (IQA) is one of the hot research areas in the field of image processing. For the reason that human being is the final receiver of the image, the image quality assessment should match the characteristics of human visual system. In this paper, we propose a novel method of image quality assessment which uses the visual selective attention of human visual system. For an image of a certain category, our method firstly detects the object in it and then calculate the saliency of the object. Lastly we use the combination of the detector’s score and the saliency as the image quality assessment. Experiments on some images of Pascal VOC dataset and INRIA dataset show that our method does well in image quality assessment.

scholarly journals Benchmarking of objective quality metrics for HDR image quality assessment

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Philippe Hanhart ◽  
Marco V. Bernardo ◽  
Manuela Pereira ◽  
António M. G. Pinheiro ◽  
Touradj Ebrahimi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Quality Metrics ◽  
Objective Quality ◽  
Hdr Image

scholarly journals On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

2018 ◽  
Vol 4 (10) ◽  
pp. 114 ◽  
Author(s):  
Pedro Garcia Freitas ◽  
Luísa da Eira ◽  
Samuel Santos ◽  
Mylene Farias
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Binary Patterns ◽  
Reference Image ◽  
Fundamental Interest ◽  
Wide Range ◽  
Estimate Quality

Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.

Image Quality Assessment Using Computer Vision

2019 ◽  
Vol 2019 (10) ◽  
pp. 317-1-317-5
Author(s):  
Zhi Li ◽  
Palghat Ramesh ◽  
Chu-Heng Liu
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

scholarly journals Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 175
Author(s):  
Ghislain Takam Tchendjou ◽  
Emmanuel Simeu
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Visual Quality ◽  
Machine Learning Techniques ◽  
Reference Image ◽  
Training Phase ◽  
Visual Quality Assessment ◽  
Learning Techniques ◽  
Direct Learning

This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main processes have been used to build our models: The first one uses deep learning with a convolutional neural network process, without any preprocessing. The second objective visual quality is computed by pooling several image features extracted from different concepts: the natural scene statistic in the spatial domain, the gradient magnitude, the Laplacian of Gaussian, as well as the spectral and spatial entropies. The features extracted from the image file are used as the input of machine learning techniques to build the models that are used to estimate the visual quality level of any image. For the machine learning training phase, two main processes are proposed: The first proposed process consists of a direct learning using all the selected features in only one training phase, named direct learning blind visual quality assessment DLBQA. The second process is an indirect learning and consists of two training phases, named indirect learning blind visual quality assessment ILBQA. This second process includes an additional phase of construction of intermediary metrics used for the construction of the prediction model. The produced models are evaluated on many benchmarks image databases as TID2013, LIVE, and LIVE in the wild image quality challenge. The experimental results demonstrate that the proposed models produce the best visual perception quality prediction, compared to the state-of-the-art models. The proposed models have been implemented on an FPGA platform to demonstrate the feasibility of integrating the proposed solution on an image sensor.

scholarly journals Subjective and Objective Quality Assessment of Swimming Pool Images

2022 ◽  
Vol 15 ◽  
Author(s):  
Fei Lei ◽  
Shuhan Li ◽  
Shuangyi Xie ◽  
Jing Liu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Swimming Pool ◽  
Target Area ◽  
Quality Models ◽  
Objective Quality ◽  
Objective Quality Assessment ◽  
Pool Image

As the research basis of image processing and computer vision research, image quality evaluation (IQA) has been widely used in different visual task fields. As far as we know, limited efforts have been made to date to gather swimming pool image databases and benchmark reliable objective quality models, so far. To filled this gap, in this paper we reported a new database of underwater swimming pool images for the first time, which is composed of 1500 images and associated subjective ratings recorded by 16 inexperienced observers. In addition, we proposed a main target area extraction and multi-feature fusion image quality assessment (MM-IQA) for a swimming pool environment, which performs pixel-level fusion for multiple features of the image on the premise of highlighting important detection objects. Meanwhile, a variety of well-established full-reference (FR) quality evaluation methods and partial no-reference (NR) quality evaluation algorithms are selected to verify the database we created. Extensive experimental results show that the proposed algorithm is superior to the most advanced image quality models in performance evaluation and the outcomes of subjective and objective quality assessment of most methods involved in the comparison have good correlation and consistency, which further indicating indicates that the establishment of a large-scale pool image quality assessment database is of wide applicability and importance.

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