Journal articles: '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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WANG, YUQING, MING ZHU, HAOCHEN PANG, and YONG WANG. "QUATERNION BASED COLOR IMAGE QUALITY ASSESSMENT INDEX." International Journal of Image and Graphics 11, no. 02 (April 2011): 195–206. http://dx.doi.org/10.1142/s0219467811004111.

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A quaternion model for describing color image is proposed in order to evaluate its quality. Local variance distribution of luminance layer is calculated. Color information is taken into account by using quaternion matrix. The description method is a combination of luminance layer and color information. The angle between the singular value feature vectors of the quaternion matrices corresponding to the reference image and the distorted image is used to measure the structural similarity of the two color images. When the reference image and distorted images are of unequal size it can also assess their quality. Results from experiments show that the proposed method is better consistent with the human visual characteristics than MSE, PSNR and MSSIM. The resized distorted images can also be assessed rationally by this method.

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Gupta, Praful, Christos Bampis, Jack Glover, Nicholas Paulter, and Alan Bovik. "Multivariate Statistical Approach to Image Quality Tasks." Journal of Imaging 4, no. 10 (October 12, 2018): 117. http://dx.doi.org/10.3390/jimaging4100117.

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Many existing natural scene statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here, we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus, we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, which facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality-relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.

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Sun, Wen, Fei Zhou, and Qingmin Liao. "MDID: A multiply distorted image database for image quality assessment." Pattern Recognition 61 (January 2017): 153–68. http://dx.doi.org/10.1016/j.patcog.2016.07.033.

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Lamichhane, Kamal, Marco Carli, and Federica Battisti. "Saliency-based deep blind image quality assessment." Electronic Imaging 2021, no. 9 (January 18, 2021): 225–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-225.

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Assessing the quality of images is a challenging task. To achieve this goal, the images must be evaluated by a pool of subjects following a well-defined assessment protocol or an objective quality metric must be defined. In this contribution, an objective metric based on neural networks is proposed. The model takes into account the human vision system by computing a saliency map of the image under test. The system is based on two modules: the first one is trained using normalized distorted images. It learns the features from the original and the distorted images and the estimated saliency map. Furthermore, an estimate of the prediction error is performed. The second module (non-linear regression module) is trained with the available subjective scores. The performances of the proposed metric have been evaluated by using state of the art quality assessment datasets. The achieved results show the effectiveness of the proposed system in matching the subjective quality score.

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Pang, Lu Lu, Cong Li Li, De Ning Qi, and Tao Zou. "A New Image Quality Assessment Method Based on SSIM and TV Model." Applied Mechanics and Materials 65 (June 2011): 542–50. http://dx.doi.org/10.4028/www.scientific.net/amm.65.542.

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In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

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Guan, Xiaodi, Lijun He, Mengyue Li, and Fan Li. "Entropy Based Data Expansion Method for Blind Image Quality Assessment." Entropy 22, no. 1 (December 31, 2019): 60. http://dx.doi.org/10.3390/e22010060.

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Image quality assessment (IQA) is a fundamental technology for image applications that can help correct low-quality images during the capture process. The ability to expand distorted images and create human visual system (HVS)-aware labels for training is the key to performing IQA tasks using deep neural networks (DNNs), and image quality is highly sensitive to changes in entropy. Therefore, a new data expansion method based on entropy and guided by saliency and distortion is proposed in this paper. We introduce saliency into a large-scale expansion strategy for the first time. We regionally add distortion to a set of original images to obtain a distorted image database and label the distorted images using entropy. The careful design of the distorted images and the entropy-based labels fully reflects the influences of both saliency and distortion on quality. The expanded database plays an important role in the application of a DNN for IQA. Experimental results on IQA databases demonstrate the effectiveness of the expansion method, and the network’s prediction effect on the IQA databases is found to be improved compared with its predecessor algorithm. Therefore, we conclude that a data expansion approach that fully reflects HVS-aware quality factors is beneficial for IQA. This study presents a novel method for incorporating saliency into IQA, namely, representing it as regional distortion.

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Corchs, Silvia, Francesca Gasparini, and Raimondo Schettini. "No reference image quality classification for JPEG-distorted images." Digital Signal Processing 30 (July 2014): 86–100. http://dx.doi.org/10.1016/j.dsp.2014.04.003.

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Wu, Yadong, Hongying Zhang, and Ran Duan. "Total Variation Based Perceptual Image Quality Assessment Modeling." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/294870.

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Visual quality measure is one of the fundamental and important issues to numerous applications of image and video processing. In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. The proposed measure compares TVs between a distorted image and its reference image to represent the loss of image structural information. Because of the good performance of TV model in describing edges, the proposed TVPIQA measure can illustrate image structure information very well. In addition, the energy of enclosed regions in a difference image between the reference image and its distorted image is used to measure the missing luminance information which is sensitive to human visual system. Finally, we validate the performance of TVPIQA measure with Cornell-A57, IVC, TID2008, and CSIQ databases and show that TVPIQA measure outperforms recent state-of-the-art image quality assessment measures.

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Li, Jun Feng, Wen Zhan Dai, and Hui Jiao Wang. "Image Quality Assessment Based on Fuzzy Similarity Measure and Wavelet Transform." Advanced Materials Research 181-182 (January 2011): 31–36. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.31.

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Based on the characteristics of wavelet coefficients of images and fuzzy similarity measure, a novel image quality assessment is proposed in this paper. Firstly, the reference image and the distorted images are decomposed into several levels by means of wavelet transform respectively. The approximation and detail coefficients of the reference image (the distorted images) are as the reference sequences (the comparative sequences). Secondly, select the right membership function to map the referenced sequences and the comparative sequences to a membership value between 0 and 1 respectively. And calculate the fuzzy similarity measure values between the reference sequences and the comparative sequences respectively. Moreover, image quality assessment matrix of every distorted image can be constructed based on the fuzzy similarity measure values and image quality can be assessed. The algorithm makes full use of perfect integral comparison mechanism of fuzzy similarity measure and the well matching of discrete wavelet transform with multi-channel model of human visual system. Experimental results show that the proposed algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system than the traditional method PSNR.

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HE, LIHUO, WEN LU, XINBO GAO, DACHENG TAO, and XUELONG LI. "A NOVEL METRIC BASED ON MCA FOR IMAGE QUALITY." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 05 (September 2011): 743–57. http://dx.doi.org/10.1142/s0219691311004298.

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Considering that the Human Visual System (HVS) has different perceptual characteristics for different morphological components, a novel image quality metric is proposed by incorporating Morphological Component Analysis (MCA) and HVS, which is capable of assessing the image with different kinds of distortion. Firstly, reference and distorted images are decomposed into linearly combined texture and cartoon components by MCA respectively. Then these components are turned into perceptual features by Just Noticeable Difference (JND) which integrates masking features, luminance adaptation and Contrast Sensitive Function (CSF). Finally, the discrimination between reference and distorted images perceptual features is quantified using a pooling strategy before the final image quality is obtained. Experimental results demonstrate that the performance of the proposed prevails over some existing methods on LIVE database II.

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Varga, Domonkos. "No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features." Electronics 10, no. 22 (November 12, 2021): 2768. http://dx.doi.org/10.3390/electronics10222768.

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No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.

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Zhang, Yin, Xuehan Bai, Junhua Yan, Yongqi Xiao, C. R. Chatwin, R. C. D. Young, and P. Birch. "No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics." Journal of Imaging Science and Technology 64, no. 1 (January 1, 2020): 10505–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.1.010505.

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Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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Tsai, Pei-Fen, Huai-Nan Peng, Chia-Hung Liao, and Shyan-Ming Yuan. "Full-Reference Image Quality Assessment with Transformer and DISTS." Mathematics 11, no. 7 (March 26, 2023): 1599. http://dx.doi.org/10.3390/math11071599.

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To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

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De, Kanjar, and Masilamani V. "NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION." Image Analysis & Stereology 37, no. 2 (July 9, 2018): 105. http://dx.doi.org/10.5566/ias.1534.

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Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available.

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Li, Yu, and Lizhuang Liu. "Image quality classification algorithm based on InceptionV3 and SVM." MATEC Web of Conferences 277 (2019): 02036. http://dx.doi.org/10.1051/matecconf/201927702036.

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In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different design choices, ranging from the use of various CNN architectures to the use of features extracted from different layers of a CNN model. To cope with the problem of a lack of adequate amounts of distorted picture data, a novel training strategy of multi-scale training, which is selecting a new image size for training after several batches, combined with data augmentation is introduced. The experimental results tested on the actual monitoring video images shows that the proposed model can accurately classify distorted images.

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DENG, CHENG, JIE LI, YIFAN ZHANG, DONGYU HUANG, and LINGLING AN. "AN IMAGE QUALITY METRIC BASED ON BIOLOGICALLY INSPIRED FEATURE MODEL." International Journal of Image and Graphics 11, no. 02 (April 2011): 265–79. http://dx.doi.org/10.1142/s0219467811004093.

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Objective image quality assessment (IQA) metrics have been widely applied to imaging systems to preserve and enhance the perceptual quality of images being processed and transmitted. In this paper, we present a novel IQA metric based on biologically inspired feature model (BIFM) and structural similarity index (SSIM). The SSIM index map is first generated through the well-known IQA metric SSIM between the reference image and the distorted image. Then, saliency map of the distorted image is extracted via BIF to define the most salient image locations. Finally, according to the saliency map, a feature weighting model is employed to define the different weights for the different samples in the SSIM index map. Experimental results confirm that the proposed IQA metric improves the performance over PSNR and SSIM under various distortion types in terms of different evaluation criteria.

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Yang, Hongtao, Ping Shi, Dixiu Zhong, Da Pan, and Zefeng Ying. "Blind Image Quality Assessment of Natural Distorted Image Based on Generative Adversarial Networks." IEEE Access 7 (2019): 179290–303. http://dx.doi.org/10.1109/access.2019.2957235.

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Ahmed, Ismail Taha, Chen Soong Der, Baraa Tareq Hammad, and Norziana Jamil. "Contrast-distorted image quality assessment based on curvelet domain features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2595. http://dx.doi.org/10.11591/ijece.v11i3.pp2595-2603.

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Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features.

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Hsu, Shau-Wei, Yu-Ta Chen, Bao-Jen Pong, and Sheng-Tzung Kuo. "Correlations of image quality metrics studied using systematically distorted videos." Optical Review 18, no. 1 (January 2011): 157–61. http://dx.doi.org/10.1007/s10043-011-0015-1.

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Yang, Xiaohan, Fan Li, Wei Zhang, and Lijun He. "Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT domain." Entropy 20, no. 11 (November 17, 2018): 885. http://dx.doi.org/10.3390/e20110885.

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Blind/no-reference image quality assessment is performed to accurately evaluate the perceptual quality of a distorted image without prior information from a reference image. In this paper, an effective blind image quality assessment approach based on entropy differences in the discrete cosine transform domain for natural images is proposed. Information entropy is an effective measure of the amount of information in an image. We find the discrete cosine transform coefficient distribution of distorted natural images shows a pulse-shape phenomenon, which directly affects the differences of entropy. Then, a Weibull model is used to fit the distributions of natural and distorted images. This is because the Weibull model sufficiently approximates the pulse-shape phenomenon as well as the sharp-peak and heavy-tail phenomena of natural scene statistics rules. Four features that are related to entropy differences and human visual system are extracted from the Weibull model for three scaling images. Image quality is assessed by the support vector regression method based on the extracted features. This blind Weibull statistics algorithm is thoroughly evaluated using three widely used databases: LIVE, TID2008, and CSIQ. The experimental results show that the performance of the proposed blind Weibull statistics method is highly consistent with that of human visual perception and greater than that of the state-of-the-art blind and full-reference image quality assessment methods in most cases.

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LU, WEN, XINBO GAO, DACHENG TAO, and XUELONG LI. "A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 04 (July 2008): 541–51. http://dx.doi.org/10.1142/s0219691308002501.

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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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Shang, Xiaobao, Xinyu Zhao, and Yong Ding. "Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains." Journal of Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1214697.

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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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Luo, Xiaoyan, Shining Wang, and Ding Yuan. "Subjective Score Predictor: A New Evaluation Function of Distorted Image Quality." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1243410.

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Image quality assessment (IQA) is a method to evaluate the perceptual performance of image. Many objective IQA algorithms are developed from the objective comparison of image features, which are mainly trained and evaluated from the ground truth of subjective scores. Due to the inconsistent experiment conditions and cumbersome observing processes of subjective experiments, it is imperative to generate the ground truth for IQA research via objective computation methods. In this paper, we propose a subjective score predictor (SSP) aiming to provide the ground truth of IQA datasets. In perfect accord with distortion information, the distortion strength of distorted image is employed as a dependent parameter. To further be consistent with subjective opinion, on the one hand, the subjective score of source image is viewed as a quality base value, and, on the other hand, we integrate the distortion parameter and the quality base value into a human visual model function to obtain the final SSP value. Experimental results demonstrate the advantages of the proposed SSP in the following aspects: effective performance to reflect the distortion strength, competitive ground truth, and valid evaluation for objective IQA methods as well as subjective scores.

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Li, Chao Feng, Yi Wen Ju, and Quan Lin Hou. "A Novel No-Reference Perceptual Blur Metric." Applied Mechanics and Materials 568-570 (June 2014): 716–20. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.716.

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In this paper, we present a novel no-reference blur metric for images. The blur metric is based on analyzing image features include the mean value of phase congruency image, the entropy of phase congruency image and the distorted image, and the gradient of the distorted image. The new index does NOT need any information from reference image, and image quality estimation is accomplished by simple functional relationship between those features. Our experimental results show that the new index outperforms existing popular no-reference blurriness metric and full reference PSNR on LIVE Gaussian blurred database and IVC blurring images.

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Fu, Hao, Guojun Liu, Xiaoqin Yang, Lili Wei, and Lixia Yang. "Two Low-Level Feature Distributions Based No Reference Image Quality Assessment." Applied Sciences 12, no. 10 (May 14, 2022): 4975. http://dx.doi.org/10.3390/app12104975.

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No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (JP2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLLFD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods.

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Sai, S. V. "A method for assessing photorealistic image quality with high resolution." Computer Optics 46, no. 1 (February 2022): 121–29. http://dx.doi.org/10.18287/2412-6179-co-899.

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The article proposes a method for assessing photorealistic image quality based on a comparison of the detail coefficients in the original and distorted images. An algorithm for identifying fine structures of the original image uses operations of active pixels segmentation, which include point objects, thin lines and texture fragments. The number of active pixels is estimated by the value of a fine detail factor (FDF), which is determined by the ratio of active pixels to the total number of image pixels. The same algorithm is used to calculate the FDF of the distorted image and, further, the image quality deterioration is estimated by comparing the obtained values. Special features of the method include the fact that the identification of small structures and the segmentation of active pixels are performed in the normalized system N-CIELAB. The algorithm also takes into account the influence of false microstructures on the results of the restored image estimating. Features of the construction of neural networks SRCNN in the tasks of a qualitative increase in the image resolution with the restoration of fine structures are considered. Results of the analysis of the quality of enlarged images by the traditional metrics PSNR and SSIM, as well as by the proposed method are also presented.

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Ahmed, Ismail Taha, Chen Soong Der, Norziana Jamil, and Mohamad Afendee Mohamed. "Improve of contrast-distorted image quality assessment based on convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5604. http://dx.doi.org/10.11591/ijece.v9i6.pp5604-5614.

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<span lang="EN-US">Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI.</span>

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Hou Chunping, 侯春萍, 马彤彤 Ma Tongtong, 岳广辉 Yue Guanghui, 冯丹丹 Feng Dandan, and 刘. 月. Liu Yue. "Multiply-Distorted Image Quality Assessment Based on High-Order Phase Congruency." Laser & Optoelectronics Progress 54, no. 7 (2017): 071001. http://dx.doi.org/10.3788/lop54.071001.

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Yuan, Yuan, Hai Su, Juhua Liu, and Guoqiang Zeng. "Locally and multiply distorted image quality assessment via multi-stage CNNs." Information Processing & Management 57, no. 4 (July 2020): 102175. http://dx.doi.org/10.1016/j.ipm.2019.102175.

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Garg, Meenu, and Amandeep Verma. "An Enhanced LSDBIQ Algorithm for Full Reference Image Quality Assessment for Multi Distorted Images." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 41. http://dx.doi.org/10.23956/ijarcsse.v7i8.18.

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Image processing is an emerging technology as image is used in various fields like medical and education. Images may corrupt due to the various categories of noises. Image quality reduces because of the image acquisition or transmission. Noise reduction is the main focus to retain the quality of the image. For the removal of this noise, there are various techniques and filters. Before applying further processing on the image, noise should be removed from the image. In this paper we deal with with a practical and effectual IQA model, called LSDBIQ (local standard deviation based image quality). This metric is examined on a well known database MDID (multi distorted image dataset). Exploratory results manifest that this metric perform better than alternative techniques for the assessment of image quality and have very low computational complexity.

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Saifeldeen, Abdalmajeed, Shu Hong Jiao, and Wei Liu. "Entirely Blind Image Quality Assessment Estimator." Applied Mechanics and Materials 543-547 (March 2014): 2496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2496.

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Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.

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Wang, Yu Qing. "Local Variance Based Color Image Quality Assessment Method." Advanced Materials Research 301-303 (July 2011): 1254–59. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1254.

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In this paper, local variance is used to describe the structural information of a color image in order to assess its quality. The representation method is different from conventional models in that some information that is sensitive to human eyes is enhanced by using local variance distribution. It encodes the local variance distribution of different channels of a color image into the three imaginary parts of a quaternion. The distance between the singular value feature vectors of the source image block and the distorted image block which are described by quaternion matrices is calculated. The experimental results show that the assessment results of the proposed assessment method are more consistent with the Human Visual System than those of the conventional assessment methods.

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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." International Journal of Computer Vision and Image Processing 2, no. 1 (January 2012): 1–15. http://dx.doi.org/10.4018/ijcvip.2012010101.

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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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WANG Chun-zhe, 王春哲, 安军社 AN Jun-she, 姜秀杰 JIANG Xiu-jie, 李杰 LI Jie, and 张羽丰 ZHANG Yu-feng. "Multi-distorted image quality assessment algorithm based on sparse representation and SOM." Chinese Journal of Liquid Crystals and Displays 33, no. 10 (2018): 877–83. http://dx.doi.org/10.3788/yjyxs20183310.0877.

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Dutta, Manoj Kumar, Mohineet Kaur, and Ram Krishna Sarkar. "Image quality improvement of old and distorted artworks using fuzzy logic technique." Optik 249 (January 2022): 168252. http://dx.doi.org/10.1016/j.ijleo.2021.168252.

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Lee, Dong Won, Seonah Kim, and Dong Yung Cho. "Obesity-Related Quality of Life and Distorted Self-Body Image in Adults." Applied Research in Quality of Life 8, no. 1 (June 9, 2012): 87–100. http://dx.doi.org/10.1007/s11482-012-9174-x.

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De Silva, Kalupahanage Dilusha Malintha, and Hyo Jong Lee. "Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning." Applied Sciences 13, no. 11 (June 4, 2023): 6816. http://dx.doi.org/10.3390/app13116816.

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Aerial images are important for monitoring land cover and land resource management. An aerial imaging source which keeps its position at a higher altitude, and which has a considerable duration of airtime, employs wireless communications for sending images to relevant receivers. An aerial image must be transmitted from the image source to a ground station where it can be stored and analyzed. Due to transmission errors, aerial images which are received from an image transmitter contain distortions which can affect the quality of the images, causing noise, color shifts, and other issues that can impact the accuracy of semantic segmentation and the usefulness of the information contained in the images. Current semantic segmentation methods discard distorted images, which makes the available dataset small or treats them as normal images, which causes poor segmentation results. This paper proposes a deep-learning-based semantic segmentation method for distorted aerial images. For different receivers, distortions occur differently, and by considering the receiver specificness of the distortions, the proposed method was able to grasp the acceptability for a distorted image using semantic segmentation models trained with large aerial image datasets to build a combined model that can effectively segment a distorted aerial image which was received by an analog image receiver. Two combined deep learning models, an approximating model, and a segmentation model were trained combinedly to maximize the segmentation score for distorted images. The results showed that the combined learning method achieves higher intersection-over-union (IoU) scores than the results obtained by using only a segmentation model.

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Varga, Domonkos. "A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps." Algorithms 13, no. 12 (November 28, 2020): 313. http://dx.doi.org/10.3390/a13120313.

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The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.

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Chen, Fan, Hong Fu, Hengyong Yu, and Ying Chu. "Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality." Sensors 23, no. 10 (May 22, 2023): 4974. http://dx.doi.org/10.3390/s23104974.

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Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of the human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and the dorsal pathway of the HVS, a dual-pathway convolutional neural network is proposed for BIQA tasks. The proposed method consists of two pathways: the “what” pathway, which mimics the ventral pathway of the HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of the HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, gradient images weighted by contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. Experiments conducted on six databases show that the proposed method achieves state-of-the-art performance.

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Okarma, Krzysztof, Piotr Lech, and Vladimir V. Lukin. "Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images." Electronics 10, no. 18 (September 14, 2021): 2256. http://dx.doi.org/10.3390/electronics10182256.

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In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.

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Journal articles on the topic 'Authentically Distorted Image Quality'

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