Relevant bibliographies by topics / Authentically Distorted Image Quality

Academic literature on the topic 'Authentically Distorted Image Quality'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Authentically Distorted Image Quality"

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Guan, Xiaodi, Fan Li, and Lijun He. "Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels." Electronics 9, no. 2 (February 3, 2020): 252. http://dx.doi.org/10.3390/electronics9020252.

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In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obtain large numbers of proxy labels which denote the quality score of authentically distorted images by using a traditional no-reference IQA method. Then the deep network is trained by the proxy labels, in order to learn IQA-related knowledge from the amounts of images with their scores. Ultimately, we use fine-tuning to inherit knowledge represented in the trained network. During the procedure, the mapping relationship fits in with human visual perception closer. The experimental results demonstrate that the proposed algorithm shows an outstanding performance as compared with the existing algorithms. On the LIVE In the Wild Image Quality Challenge database and KonIQ-10k database (two standard databases for authentically distorted image quality assessment), the algorithm realized good consistency between human visual perception and the predicted quality score of authentically distorted images.
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Celona, Luigi, and Raimondo Schettini. "Blind quality assessment of authentically distorted images." Journal of the Optical Society of America A 39, no. 6 (March 2, 2022): B1. http://dx.doi.org/10.1364/josaa.448144.

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Jiang, Qiuping, Zhenyu Peng, Sheng Yang, and Feng Shao. "Authentically Distorted Image Quality Assessment by Learning From Empirical Score Distributions." IEEE Signal Processing Letters 26, no. 12 (December 2019): 1867–71. http://dx.doi.org/10.1109/lsp.2019.2951533.

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Varga, Domonkos. "No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features." Journal of Imaging 8, no. 6 (June 19, 2022): 173. http://dx.doi.org/10.3390/jimaging8060173.

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With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.
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Chen, Fan, Hong Fu, Hengyong Yu, and Ying Chu. "No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network." Applied Sciences 13, no. 11 (June 3, 2023): 6802. http://dx.doi.org/10.3390/app13116802.

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When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model’s ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.
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Tang, Yiling, Shunliang Jiang, Shaoping Xu, Tingyun Liu, and Chongxi Li. "Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space." Applied Sciences 9, no. 12 (June 19, 2019): 2499. http://dx.doi.org/10.3390/app9122499.

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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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Han, Lintao, Hengyi Lv, Yuchen Zhao, Hailong Liu, Guoling Bi, Zhiyong Yin, and Yuqiang Fang. "Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment." Sensors 23, no. 1 (December 30, 2022): 427. http://dx.doi.org/10.3390/s23010427.

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To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.
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Ghadiyaram, Deepti, and Alan C. Bovik. "Perceptual quality prediction on authentically distorted images using a bag of features approach." Journal of Vision 17, no. 1 (January 27, 2017): 32. http://dx.doi.org/10.1167/17.1.32.

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WANG Chun-zhe, 王春哲, 李杰 LI Jie, 李明晶 LI Ming-jing, and 郭盼 GUO Pan. "Image quality assessment algorithm for multi-distorted image." Chinese Journal of Liquid Crystals and Displays 30, no. 4 (2015): 681–86. http://dx.doi.org/10.3788/yjyxs20153004.0681.

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Zhang, Yin, Xuehan Bai, Junhua Yan, Yongqi Xiao, Wanyi Zhang, C. R. Chatwin, and R. C. D. Young. "A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information." Journal of Imaging Science and Technology 63, no. 6 (November 1, 2019): 60504–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.6.060504.

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Abstract A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.
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Books on the topic "Authentically Distorted Image Quality"

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Grant, Jon E., Eric W. Leppink, and Sarah A. Redden. The Relationship Between Body Dysmorphic Disorder and Eating Disorders. Edited by Katharine A. Phillips. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190254131.003.0036.

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This chapter discusses research findings regarding body dysmorphic disorder (BDD) and eating disorders, and it provides guidelines for distinguishing between them. BDD and eating disorders show many similarities, including negative and distorted body image, decreased quality of life, compensatory behaviors such as dieting, and abnormalities in visual processing. Patients with BDD express specific concerns with different parts of their bodies and physical appearance; common examples are complexion, nose, breasts/genitals, and hair. In patients who have prominent concerns about weight and body fat and shape, however, the diagnosis of BDD can be complicated because such concerns can occur as a symptom of BDD but also overlap with those in eating disorders such as anorexia nervosa and bulimia nervosa. BDD and eating disorders are often comorbid, which is accompanied by notably higher rates of suicidality and psychiatric hospitalization than occur in patients with either disorder alone. BDD and eating disorders represent distinct pathologies, and it is important to distinguish between them, particularly given the increased risk of suicidality when the disorders are comorbid.
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Book chapters on the topic "Authentically Distorted Image Quality"

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Chetouani, Aladine. "An Image Quality Metric with Reference for Multiply Distorted Image." In Advanced Concepts for Intelligent Vision Systems, 477–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48680-2_42.

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Zhu, Yiming, Xianzhi Chen, and Shengkui Dai. "No-Reference Image Quality Assessment for Contrast Distorted Images." In Lecture Notes in Computer Science, 241–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_20.

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Mansoor, Atif Bin, and Adeel Anwar. "Subjective Evaluation of Image Quality Measures for White Noise Distorted Images." In Advanced Concepts for Intelligent Vision Systems, 10–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17688-3_2.

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Ahmed Seghir, Zianou, and Fella Hachouf. "Image Quality Assessment Based on Edge-Region Information and Distorted Pixel for JPEG and JPEG2000." In Advanced Concepts for Intelligent Vision Systems, 156–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04697-1_15.

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Ahmed, Basma, Osama A. Omer, Amal Rashed, Domenec Puig, and Mohamed Abdel-Nasser. "Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220345.

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Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.
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De, Indrajit. "A Fuzzy Relational Classifier Based Image Quality Assessment Method." In Intelligent Analysis of Multimedia Information, 247–65. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0498-6.ch009.

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Fuzzy classification techniques are used for image classification for quite a long time back by allowing pixels to have membership in more than one class. However, handling information at the pixel level is time consuming and there is a high chance of biased assessment of images if class labels are assigned by a single human observer. Even considering multiple observers' opinions don't able to reflect an individual's perception in assessing quality of images, if it is crisp. In this chapter, the fuzzy relational classifier (FRC) is used to assess quality of images distorted by information loss or noise, unlike the earlier methods where images are preprocessed to remove the noise before classification.
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Abdelouahad, Abdelkaher Ait, Mohammed El Hassouni, Hocine Cherifi, and Driss Aboutajdine. "A New Image Distortion Measure Based on Natural Scene Statistics Modeling." In Geographic Information Systems, 616–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch037.

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In the field of Image Quality Assessment (IQA), this paper examines a Reduced Reference (RRIQA) measure based on the bi-dimensional empirical mode decomposition. The proposed measure belongs to Natural Scene Statistics (NSS) modeling approaches. First, the reference image is decomposed into Intrinsic Mode Functions (IMF); the authors then use the Generalized Gaussian Density (GGD) to model IMF coefficients distribution. At the receiver side, the same number of IMF is computed on the distorted image, and then the quality assessment is done by fitting error between the IMF coefficients histogram of the distorted image and the GGD estimate of IMF coefficients of the reference image, using the Kullback Leibler Divergence (KLD). In addition, the authors propose a new Support Vector Machine-based classification approach to evaluate the performances of the proposed measure instead of the logistic function-based regression. Experiments were conducted on the LIVE dataset.
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De Silva, P., K. Abhiram, and V. Tavakkoli. "A Comprehensive Analysis of Document-Image Distortions and their Respective Impact on Distorted Text/Character-Image Recognition Quality." In Autonomous Systems 2019: An Almanac, 176–98. VDI Verlag, 2019. http://dx.doi.org/10.51202/9783186864109-176.

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Guo, Yingchun, Gang Yan, Cuihong Xue, and Yang Yu. "Blind Assessment of Wavelet-Compressed Images Based on Subband Statistics of Natural Scenes." In Computer Vision, 1322–37. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch055.

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This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.
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Conference papers on the topic "Authentically Distorted Image Quality"

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Yang, Luping, Haiqing Du, Jingtao Xu, and Yong Liu. "Blind image quality assessment on authentically distorted images with perceptual features." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532717.

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Ghadiyaram, Deepti, and Alan C. Bovik. "Feature maps driven no-reference image quality prediction of authentically distorted images." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Huib de Ridder. SPIE, 2015. http://dx.doi.org/10.1117/12.2084807.

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Babu, Nithin C., Vignesh Kannan, and Rajiv Soundararajan. "No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images." In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00249.

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Ghadiyaram, Deepti, and Alan C. Bovik. "Scene statistics of authentically distorted images in perceptually relevant color spaces for blind image quality assessment." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351526.

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Sakuldee, Ratchakit, and Somkait Udomhunsakul. "Objective Measurements of Distorted Image Quality Evaluation." In 2008 International Conference on Computer and Communication Engineering. IEEE, 2008. http://dx.doi.org/10.1109/iccce.2008.4580768.

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Lin, Ching-Ti, Tsung-Jung Liu, and Kuan-Hsien Liu. "Visual quality prediction on distorted stereoscopic images." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296929.

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Wu, Jun, Zhaoqiang Xia, Yifeng Ren, and Huifang Li. "No-reference quality assessment for contrast-distorted image." In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. http://dx.doi.org/10.1109/ipta.2016.7820968.

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Shen, Yinghua, Chaohui Lu, and Hui Ren. "Objective quality assessment of JPEG distorted stereoscopic image." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6526080.

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Bosse, Sebastian, Mischa Siekmann, Jennifer Rasch, Thomas Wiegand, and Wojciech Samek. "Quality assessment of image patches distorted by image compression using crowdsourcing." In 2016 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2016. http://dx.doi.org/10.1109/icme.2016.7552958.

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Xu, Min, and Zhiming Wang. "No-reference quality assessment of contrast-distorted images." In 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, 2016. http://dx.doi.org/10.1109/siprocess.2016.7888285.

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