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.

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Chenggang Yan ◽  
Tong Teng ◽  
Yutao Liu ◽  
Yongbing Zhang ◽  
Haoqian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Gaussian White Noise ◽  
The State ◽  
Reference Image

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

Halftone Image Quality Assessment Based on Digital Watermark

2012 ◽  
Vol 262 ◽  
pp. 181-185
Author(s):  
Yun Fei Zhong ◽  
Lu Jing Fu ◽  
Jie Zou ◽  
Rong Hua Deng
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Information Hiding ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Digital Watermark ◽  
Halftone Image ◽  
Evaluation Scheme ◽  
Segmented Image ◽  
Segmentation Image

Digital watermarking is the process of computer-aided information hiding in a carrier signal with robust and concealment characteristics. A halftone image quality assessment framework based on digital watermark is proposed. First, we segmented image dark tong area and edge through C-V model and transformed segmentation image into binary image to obtain the area in which watermark is to be embedded. Then, the watermark was embedded and the image quality assessment was completed by extracted watermark. The experimental results show that the presented quality evaluation scheme of halftone image can detect printed image quality efficiently with the characteristics of novel and rapid.

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.

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.

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 A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

2016 ◽  
Vol 16 (6) ◽  
pp. 316-325 ◽  
Author(s):  
Mariusz Oszust
Keyword(s):  
Image Quality ◽  
Visual Perception ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Human Subjects ◽  
Reference Image ◽  
Human Visual Perception ◽  
Full Reference ◽  
Highly Correlated

Abstract The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaobao Shang ◽  
Xinyu Zhao ◽  
Yong Ding
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Support Vector ◽  
Subjective Perception ◽  
Edge Information ◽  
Objective Quality ◽  
Reference Images ◽  
Joint Quality ◽  
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.

scholarly journals Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

2019 ◽  
Vol 25 (5) ◽  
pp. 57-62 ◽  
Author(s):  
Krzysztof Okarma ◽  
Jaroslaw Fastowicz
Keyword(s):  
Additive Manufacturing ◽  
Image Quality ◽  
Quality Assessment ◽  
Surface Quality ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Assessment Methods ◽  
Reference Image ◽  
Visual Evaluation ◽  
Full Reference

Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

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