Artykuły w czasopismach: „Subjective and Objective Image Quality Assessment”

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Park, Hyung-ju, and Dong-hwan Har. "Subjective image quality assessment based on objective image quality measurement factors." IEEE Transactions on Consumer Electronics 57, no. 3 (2011): 1176–84. http://dx.doi.org/10.1109/tce.2011.6018872.

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Afnan, Afnan, Faiz Ullah, Yaseen Yaseen, Jinhee Lee, Sonain Jamil, and Oh-Jin Kwon. "Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression." Sensors 23, no. 3 (2023): 1297. http://dx.doi.org/10.3390/s23031297.

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The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

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Zhang, Huiqing, Donghao Li, Yibing Yu, and Nan Guo. "Subjective and Objective Quality Assessments of Display Products." Entropy 23, no. 7 (2021): 814. http://dx.doi.org/10.3390/e23070814.

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In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.

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Zhang, Chun E., Fan Ci Guo, and Ke Xiong. "Towards Subjective Consistency: An Effective Objective Quality Assessment Algorithm for Binary Image." Key Engineering Materials 474-476 (April 2011): 143–50. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.143.

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Image quality assessment plays an important role in various image processing applications. One of the challenges to objectively assess image quality is how to design an effective scheme to achieve high consistency with the classic subjective image assessment criterion, Mean Opinion Score (MOS). This work presents a novel objective assessment algorithm for binary images by considering three factors which have great influences on visual quality of binary images, i.e., structural change caused by noise point, isolated noise points, and gathering noise points. Experimental results show that our algorithm can achieve effective objective assessment results with higher consistency with the MOS criterion.

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Zelmati, Omar, Boban Bondžulić, Boban Pavlović, Ivan Tot, and Saad Merrouche. "Study of subjective and objective quality assessment of infrared compressed images." Journal of Electrical Engineering 73, no. 2 (2022): 73–87. http://dx.doi.org/10.2478/jee-2022-0011.

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Abstract Given the lack of accessible infrared compressed images’ benchmarks annotated by human subjects, this work presents a new database with the aim of studying both subjective and objective image quality assessment (IQA) on compressed long wavelength infrared (LWIR) images. The database contains 20 reference (pristine) images and 200 distorted (degraded) images obtained by application of the most known compression algorithms used in multimedia and communication fields, namely: JPEG and JPEG-2000. Each compressed image is evaluated by 31 subjects having different levels of experience in LWIR images. Mean opinion scores (MOS) and natural scene statistics (NSS) of pristine and compressed images are elaborated to study the performance of the database. Five analyses are conducted on collected images and subjective scores, namely: analysis by compression type, analysis by file size, analysis by reference image, analysis by quality level and analysis by subject. Moreover, a wide set of objective IQA metrics is applied on the images and the obtained scores are compared with the collected subjective scores. Results show that objective IQA measures correlate with human subjective results with a degree of agreement up to 95 %, so this benchmark is promising to improve existing and develop new IQA measures for compressed LWIR images. Thanks to a real-world surveillance original images based on which we analyze how image compression and quality level affect the quality of compressed images, this database is primarily suitable for (military and civilian) surveillance applications. The database is accessible via the link: https://github.com/azedomar/compressed-LWIR-images-IQA-database. As a follow-up to this work, an extension of the database is underway to study other types of distortion in addition to compression.

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Park, Hyung-Ju, and Dong-Hwan Har. "Correlation Research between Objective and Subjective Image Quality Assessment." Journal of the Korea Contents Association 11, no. 8 (2011): 68–76. http://dx.doi.org/10.5392/jkca.2011.11.8.068.

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Shrestha, Prarthana, Rik Kneepkens, Gijs van Elswijk, et al. "Objective and Subjective Assessment of Digital Pathology Image Quality." AIMS Medical Science 2, no. 1 (2015): 65–78. http://dx.doi.org/10.3934/medsci.2015.1.65.

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Good, Walter F., David Gur, John H. Feist, et al. "Subjective and objective assessment of image quality—A comparison." Journal of Digital Imaging 7, no. 2 (1994): 77–78. http://dx.doi.org/10.1007/bf03168426.

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Oszust, Mariusz. "No-Reference Image Quality Assessment with Local Gradient Orientations." Symmetry 11, no. 1 (2019): 95. http://dx.doi.org/10.3390/sym11010095.

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Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.

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Yang, Yang, Jun Ming, and Nenghai Yu. "Color Image Quality Assessment Based on CIEDE2000." Advances in Multimedia 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/273723.

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Combining the color difference formula of CIEDE2000 and the printing industry standard for visual verification, we present an objective color image quality assessment method correlated with subjective vision perception. An objective score conformed to subjective perception (OSCSP)Qwas proposed to directly reflect the subjective visual perception. In addition, we present a general method to calibrate correction factors of color difference formula under real experimental conditions. Our experiment results show that the present DE2000-based metric can be consistent with human visual system in general application environment.

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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 (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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Xiang, Tao, Ying Yang, and Shangwei Guo. "Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches." IEEE Transactions on Multimedia 22, no. 5 (2020): 1259–72. http://dx.doi.org/10.1109/tmm.2019.2938612.

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Lamichhane, Kamal, Marco Carli, and Federica Battisti. "Saliency-based deep blind image quality assessment." Electronic Imaging 2021, no. 9 (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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Okarma, Krzysztof, Wojciech Chlewicki, Mateusz Kopytek, Beata Marciniak, and Vladimir Lukin. "Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images." Entropy 23, no. 11 (2021): 1525. http://dx.doi.org/10.3390/e23111525.

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

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Wang, Zhiyu, Jiayan Zhuang, Sichao Ye, Ningyuan Xu, Jiangjian Xiao, and Chengbin Peng. "Image Restoration Quality Assessment Based on Regional Differential Information Entropy." Entropy 25, no. 1 (2023): 144. http://dx.doi.org/10.3390/e25010144.

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With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.

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Dixon, Timothy D., Eduardo Fernández Canga, Stavri G. Nikolov, et al. "Selection of image fusion quality measures: objective, subjective, and metric assessment." Journal of the Optical Society of America A 24, no. 12 (2007): B125. http://dx.doi.org/10.1364/josaa.24.00b125.

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Wu, Qingbo, Lei Wang, King Ngi Ngan, Hongliang Li, Fanman Meng, and Linfeng Xu. "Subjective and Objective De-Raining Quality Assessment Towards Authentic Rain Image." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 11 (2020): 3883–97. http://dx.doi.org/10.1109/tcsvt.2020.2972566.

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Maksimović-Moićević, Sanja, Željko Lukač, and Miodrag Temerinac. "Objective estimation of subjective image quality assessment using multi-parameter prediction." IET Image Processing 13, no. 13 (2019): 2428–35. http://dx.doi.org/10.1049/iet-ipr.2018.6143.

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Hu, Bo, Leida Li, Jinjian Wu, and Jiansheng Qian. "Subjective and objective quality assessment for image restoration: A critical survey." Signal Processing: Image Communication 85 (July 2020): 115839. http://dx.doi.org/10.1016/j.image.2020.115839.

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Fang, Yuming, Liping Huang, Jiebin Yan, Xuelin Liu, and Yang Liu. "Perceptual Quality Assessment of Omnidirectional Images." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 580–88. http://dx.doi.org/10.1609/aaai.v36i1.19937.

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Omnidirectional images, also called 360◦images, have attracted extensive attention in recent years, due to the rapid development of virtual reality (VR) technologies. During omnidirectional image processing including capture, transmission, consumption, and so on, measuring the perceptual quality of omnidirectional images is highly desired, since it plays a great role in guaranteeing the immersive quality of experience (IQoE). In this paper, we conduct a comprehensive study on the perceptual quality of omnidirectional images from both subjective and objective perspectives. Specifically, we construct the largest so far subjective omnidirectional image quality database, where we consider several key influential elements, i.e., realistic non-uniform distortion, viewing condition, and viewing behavior, from the user view. In addition to subjective quality scores, we also record head and eye movement data. Besides, we make the first attempt by using the proposed database to train a convolutional neural network (CNN) for blind omnidirectional image quality assessment. To be consistent with the human viewing behavior in the VR device, we extract viewports from each omnidirectional image and incorporate the user viewing conditions naturally in the proposed model. The proposed model is composed of two parts, including a multi-scale CNN-based feature extraction module and a perceptual quality prediction module. The feature extraction module is used to incorporate the multi-scale features, and the perceptual quality prediction module is designed to regress them to perceived quality scores. The experimental results on our database verify that the proposed model achieves the competing performance compared with the state-of-the-art methods.

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Kazakeviciute-Januskeviciene, Giruta, Edgaras Janusonis, Romualdas Bausys, Tadas Limba, and Mindaugas Kiskis. "Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach." Remote Sensing 12, no. 24 (2020): 4152. http://dx.doi.org/10.3390/rs12244152.

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The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.

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Sazzad, Z. M. Parvez, Roushain Akhter, J. Baltes, and Y. Horita. "Objective No-Reference Stereoscopic Image Quality Prediction Based on 2D Image Features and Relative Disparity." Advances in Multimedia 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/256130.

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Stereoscopic images are widely used to enhance the viewing experience of three-dimensional (3D) imaging and communication system. In this paper, we propose an image feature and disparity dependent quality evaluation metric, which incorporates human visible system characteristics. We believe perceived distortions and disparity of any stereoscopic image are strongly dependent on local features, such as edge (i.e., nonplane areas of an image) and nonedge (i.e., plane areas of an image) areas within the image. Therefore, a no-reference perceptual quality assessment method is developed for JPEG coded stereoscopic images based on segmented local features of distortions and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness and the edge distortion within the block of images are evaluated in this method. Subjective stereo image database is used for evaluation of the metric. The subjective experiment results indicate that our metric has sufficient prediction performance.

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Pula, Michal, Emilia Kucharczyk, Agata Zdanowicz, and Maciej Guzinski. "Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination." Tomography 9, no. 4 (2023): 1485–93. http://dx.doi.org/10.3390/tomography9040118.

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In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain’s gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.

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Shi, Ran, King Ngi Ngan, Songnan Li, Raveendran Paramesran, and Hongliang Li. "Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure." IEEE Transactions on Image Processing 24, no. 12 (2015): 5033–45. http://dx.doi.org/10.1109/tip.2015.2473099.

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Shi, Guangming, Wenfei Wan, Jinjian Wu, Xuemei Xie, Weisheng Dong, and Hong Ren Wu. "SISRSet: Single image super-resolution subjective evaluation test and objective quality assessment." Neurocomputing 360 (September 2019): 37–51. http://dx.doi.org/10.1016/j.neucom.2019.06.027.

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Ma, Lin, Weisi Lin, Chenwei Deng, and King Ngi Ngan. "Image Retargeting Quality Assessment: A Study of Subjective Scores and Objective Metrics." IEEE Journal of Selected Topics in Signal Processing 6, no. 6 (2012): 626–39. http://dx.doi.org/10.1109/jstsp.2012.2211996.

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Honarmand, Amir R., Ali Shaibani, Tamila Pashaee, et al. "Subjective and objective evaluation of image quality in biplane cerebral digital subtraction angiography following significant acquisition dose reduction in a clinical setting." Journal of NeuroInterventional Surgery 9, no. 3 (2016): 297–301. http://dx.doi.org/10.1136/neurintsurg-2016-012296.

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ObjectiveDifferent technical and procedural methods have been introduced to develop low radiation dose protocols in neurointerventional examinations. We investigated the feasibility of minimizing radiation exposure dose by simply decreasing the detector dose during cerebral DSA and evaluated the comparative level of image quality using both subjective and objective methods.MethodsIn a prospective study of patients undergoing diagnostic cerebral DSA, randomly selected vertebral arteries (VA) and/or internal carotid arteries and their contralateral equivalent arteries were injected. Detector dose of 3.6 and 1.2 μGy/frame were selected to acquire standard dose (SD) and low dose (LD) images, respectively. Subjective image quality assessment was performed by two neurointerventionalists using a 5 point scale. For objective image quality evaluation, circle of Willis vessels were categorized into conducting, primary, secondary, and side branch vessels. Two blinded observers performed arterial diameter measurements in each category. Only image series obtained from VA injections opacifying the identical posterior intracranial circulation were utilized for objective assessment.ResultsNo significant difference between SD and LD images was observed in subjective and objective image quality assessment in 22 image series obtained from 10 patients. Mean reference air kerma and kerma area product were significantly reduced by 61.28% and 61.24% in the LD protocol, respectively.ConclusionsOur study highlights the necessity for reconsidering radiation dose protocols in neurointerventional procedures, especially at the level of baseline factory settings.

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Tomás, Julián Espinosa, Jorge Pérez Rodríguez, David Más Candela, Carmen Vázquez Ferri, and Esther Perales. "Objective Prediction of Human Visual Acuity Using Image Quality Metrics." Applied Sciences 13, no. 10 (2023): 6350. http://dx.doi.org/10.3390/app13106350.

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This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty.

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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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Ozcinar, Cagri, and Aakanksha Rana. "Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views." Electronic Imaging 2021, no. 9 (2021): 295–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-295.

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Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objective, full-reference quality assessment framework which studies quality measurement for ODIs generated by GAN-based and CNN-based SISR methods. The quality assessment framework offers to utilize tangential views to cope with the spherical nature of a given ODIs. The generated tangential views are distortion-free and can be efficiently scaled to high-resolution spherical data for SISR quality measurement. We extensively evaluate two state-of-the-art SISR methods using widely used full-reference SISR quality metrics adapted to our designed framework. In addition, our study reveals that most objective metric show high performance over CNN based SISR, while subjective tests favors GAN-based architectures.

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Shahkolaei, Atena, Hossein Ziaei Nafchi, Somaya Al-Maadeed, and Mohamed Cheriet. "Subjective and objective quality assessment of degraded document images." Journal of Cultural Heritage 30 (March 2018): 199–209. http://dx.doi.org/10.1016/j.culher.2017.10.001.

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Deng, Ruizhe, Yang Zhao, and Yong Ding. "Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment." Journal of Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4752378.

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

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Park, Hyung Ju, and Dong Hwan Har. "The Correlation between Image Preferences and Image Quality Factors: ISO Objective and Subjective Image Quality Assessments." International Journal of the Image 2, no. 2 (2012): 241–54. http://dx.doi.org/10.18848/2154-8560/cgp/v02i02/44251.

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Chandrakanth, T., and B. Sandhya. "Analysis of SSIM based Quality Assessment across Color Channels of Images." International Journal of System Dynamics Applications 4, no. 3 (2015): 30–42. http://dx.doi.org/10.4018/ijsda.2015070102.

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Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to a wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operations in image processing. During the last two decades, efforts have been put to design such an image quality metric which can be calculated simply but can accurately reflect subjective quality of human perception. In this paper, the authors evaluated the quality assessment of color images using SSIM (structural similarity index) metric across various color spaces. They experimented to study the effect of color spaces in metric based and distance based quality assessment. The authors proposed a metric using CIE Lab color space and SSIM, which has better correlation to the subjective assessment in a benchmark dataset.

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Bondžulić, Boban, Boban Pavlović, Nenad Stojanović, and Vladimir Petrović. "Picture-wise just noticeable difference prediction model for JPEG image quality assessment." Vojnotehnicki glasnik 70, no. 1 (2022): 62–86. http://dx.doi.org/10.5937/vojtehg70-34739.

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Introduction/purpose: The paper presents interesting research related to the performance analysis of the picture-wise just noticeable difference (JND) prediction model and its application in the quality assessment of images with JPEG compression. Methods: The performance analysis of the JND model was conducted in an indirect way by using the publicly available results of subject-rated image datasets with the separation of images into two classes (above and below the threshold of visible differences). In the performance analysis of the JND prediction model and image quality assessment, five image datasets were used, four of which come from the visible wavelength range, and one dataset is intended for remote sensing and surveillance with images from the infrared part of the electromagnetic spectrum. Results: The pap 86 er shows that using a picture-wise JND model, subjective image quality assessment scores can be estimated with better accuracy, leading to significant performance improvements of the traditional peak signal-to-noise ratio (PSNR). The gain achieved by introducing the picture-wise JND model in the objective assessment depends on the chosen dataset and the results of the initial simple to compute PSNR measure, and it was obtained on all five datasets. The mean linear correlation coefficient (for five datasets) between subjective and PSNR objective quality estimates increased from 74% (traditional PSNR) to 90% (picture-wise JND PSNR). Conclusion: Further improvement of the JND-based objective measure can be obtained by improving the picture-wise model of JND prediction.

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EL Sahili, Nabil, Ibrahim Nasseh, Antoine Berberi, Sandra David-Tchouda, Sophie Thoret, and Thomas Fortin. "Impact of Cone Beam Computed Tomography Dose in Pre-Surgical Implant Analysis." Open Dentistry Journal 12, no. 1 (2018): 94–103. http://dx.doi.org/10.2174/1874210601812010094.

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Objectives: Cone-Beam Computed Tomography (CBCT) produces vital information required for the accurate and prudent placement of dental implants. Lack of standardization between CBCT machines may result in unsafe patient exposure to harmful radiation; higher doses are not necessarily associated with improved image quality. Aim: The study aimed to assess the influence of low- and high-dose milliamperage settings on CBCT images for objective and subjective implant planning. Methods: Two dry skulls (4 hemi-maxillary segments of the maxilla and 4 hemi-maxillary segments of the mandible) were scanned under low (2 mA) and high (6.3 mA) dosage settings using a CBCT (Carestream CS 9300). Cross-sectional slices of both image qualities were evaluated by five expert clinicians, for image quality for implant planning and objective bone measurements. Results: There were no significant differences in bone measurements taken on high or low dose images (p > 0.05). In qualitative image assessments, assessment and image quality for almost all observers were independent of each other. For planning posterior mandibular implant placement, increased dosage improved concordance and kappa values between low and high dose images. Conclusion: Reduction in milliamperage did not affect diagnostic image quality for objective bone measurements and produced sufficient intra-rater reliability for qualitative assessment; therefore dose reduction can be achieved without compromising diagnostic decision- making.

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Sendjasni, Abderrezzaq, Mohamed-Chaker Larabi, and Faouzi Alaya Cheikh. "On the Improvement of 2D Quality Assessment Metrics for Omnidirectional Images." Electronic Imaging 2020, no. 9 (2020): 287–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-287.

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Subjective quality assessment remains the most reliable way to evaluate image quality while being tedious and money consuming. Therefore, objective quality evaluation ensures a trade-off by providing a computational approach for predicting image quality. Even though a large literature exists for 2D image and video quality evaluation, 360-degree images quality is still under-explored. One can question the efficiency of 2D quality metrics on such a new type of content. To this end, we propose to study the possible improvement of well-known 2D quality metrics using important features related to 360-degree content, i.e. equator bias and visual saliency. The performance evaluation is conducted on two databases containing various distortion types. The obtained results show a slight improvement of the performance highlighting some problems inherently related to both the database content and the subjective evaluation approach used to obtain the observers’ quality scores.

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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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Fante, Kinde Anlay, Fetulhak Abdurahman, and Mulugeta Tegegn Gemeda. "An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images." Transactions on Environment and Electrical Engineering 4, no. 1 (2020): 18. http://dx.doi.org/10.22149/teee.v4i1.139.

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Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.

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Frackiewicz, Mariusz, Grzegorz Szolc, and Henryk Palus. "An Improved SPSIM Index for Image Quality Assessment." Symmetry 13, no. 3 (2021): 518. http://dx.doi.org/10.3390/sym13030518.

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Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

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Wang, Shiqi, Ke Gu, Xiang Zhang, et al. "Subjective and Objective Quality Assessment of Compressed Screen Content Images." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6, no. 4 (2016): 532–43. http://dx.doi.org/10.1109/jetcas.2016.2598756.

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Zhao, Jing, and Qi Guo. "Intelligent Assessment for Visual Quality of Streets: Exploration Based on Machine Learning and Large-Scale Street View Data." Sustainability 14, no. 13 (2022): 8166. http://dx.doi.org/10.3390/su14138166.

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At present, the collection and analysis of large amounts of key data for the visual quality assessment of streets are performed manually. The assessment efficiency is not high, and the effective information is not fully explored. This study aims to establish an intelligent method for assessing the visual quality of streets. Taking the Hexi District of Tianjin as an example and using street view images as the assessment medium, an assessment model of objective physical indicators is established based on PaddleSeg, an assessment model of subjective perceptual indicators is established based on neural image assessment, and a visual quality assessment model of streets is established based on a random forest. The above models can intelligently evaluate the visual quality of streets and key indicators affecting visual quality. The influence of each key indicator on the visual quality of streets and the relationship between objective physical indicators and subjective perceptual indicators are analyzed. Through a combination of subjective and objective as well as qualitative and quantitative methods, the results show satisfactory assessment accuracy. In short, this study uses machine-learning techniques to improve the scientific rigor and efficiency of visual quality assessment and expand the scale of visual quality assessment data.

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Du, Juan. "AIVMAF: Automatic Image Quality Estimation Based on Improved VMAF and YOLOv4." Journal of Physics: Conference Series 2289, no. 1 (2022): 012020. http://dx.doi.org/10.1088/1742-6596/2289/1/012020.

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Abstract The current most widely used way of image quality estimation relies heavily on the subjective assessment, while majority of past objective estimation methods are not satisfactory on accuracy. To solve them and realize unsupervised image quality estimation with high precision, this paper creates a linear way with “Proportional Partition” controlled by horizontal and vertical rates of extracted pixel to get best representations of the image with patching, balance the uneven distribution of image quality in each source image, and offer dynamic compatibility to images with high resolution. Besides, it estimates the image quality automatically with a model trained by current best artificial intelligence (AI) algorithm for target detection YOLOv4 with 1000 images random selected from ImageNet2013 database. The proposal also uses the spirit of joint indices from the current widely used method named Video Multimethod Assessment Fusion (VMAF). But we replace its Visual Information Fidelity (VIF) with Visual Saliency-induced Index (VSI) and add VSI to our target function because of VIF’s dependence on subjective assessment, and also for VSI’s better performance surpassing most recent IQA estimators as TOP3 best model in recent world. Besides, contrast masking is also included by objective function for the KL-divergence to simulate the human visual perception better. A creative “Batch Learning” way is found to address patches for less calculation and faster speed. All source images are pretreated with colour space transformation and normalization to improve descriptiveness of images and reduce the redundant points, and a threshold is devised to formulate suppression mechanisms. The proposed solution is tested to be a good image quality assessor in many aspects such as correctness, consistency, linearity, monotonicity and speed, and performs well on even HD images.

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Ni, Jun, Zi Yin Li, and Hua Cai Chen. "No-Reference Image Quality Assessment Based on Visual Perception." Advanced Engineering Forum 1 (September 2011): 325–29. http://dx.doi.org/10.4028/www.scientific.net/aef.1.325.

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No-reference image quality assessment is an important issue for video compression and communication. This work presents a no-reference objective image/video sharpness method based on visual perception metric (VPM). The algorithm gets image typical edge and edge width firstly, and then gets gray contrast of typical edge region, finally utilizes these factors to integrate a probability summation assessment model. The proposed metric is able to predict the amount of sharpness in image with different content. Experimental results show that this method is consistent with subjective assessment of human being and can be use to describe the visual perception of image effectively.

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Zhang, Ning, and Cui Lin. "The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights." Sensors 23, no. 3 (2023): 1621. http://dx.doi.org/10.3390/s23031621.

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Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

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Amirshahi, Seyed Ali. "Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead." London Imaging Meeting 2021, no. 1 (2021): 1–4. http://dx.doi.org/10.2352/issn.2694-118x.2021.lim-1.

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

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Rusandu, Albertina, Adrian Beck, Atle Hegge, and Gabriele Engh. "Image quality in abdominal CT: A comparison of two reconstruction algorithms in Filtered Back Projection (FBP)." MEDICAL IMAGING AND RADIOTHERAPY JOURNAL 39, no. 1 (2022): 5–11. http://dx.doi.org/10.47724/mirtj.2022.i02.a001.

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Objectives: The aim of this study was to evaluate the effect of the choice of kernel on the image quality in abdominal CT images with focus on liver lesion visibility. Methods: In this comparative study 84 abdominal CT examinations of patients with liver lesions that included parallel series reconstructed with two different kernels (B30 and B45) were analyzed. The subjective assessment of image quality was performed using visual grading analysis based on anatomical criteria, liver lesion visibility and perceived image quality. Objective image quality was assessed by measurements of Hounsfield unit (HU) values (average and standard deviation) in abdominal organs and calculations of contrast-to-noise ratios (CNR). Results: B30 kernel performed significantly better than B45 in all criteria except for sharpness. The most considerable improvement of the image quality was in terms of subjective experienced image noise, overall diagnostic image quality and visually sharp reproduction of liver lesions. The physical measurements showed that CNR increased by up to 46% when using B30. Conclusions: Using a B30 kernel algorithm for image reconstruction reduces noise and by that improves image quality and diagnostic accuracy significantly when compared to B45.

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Shen, Liquan, Yang Yao, Xianqiu Geng, Ruigang Fang, and Dapeng Wu. "A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information." Sensors 23, no. 13 (2023): 6230. http://dx.doi.org/10.3390/s23136230.

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Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.

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Irsal, Muhammad, Muhammad Rival Alfajri, Vincentius Deva Ananta, Khairil Anwar, and Sriyatun Sriyatun. "Optimasi Penggunaan Faktor Eksposi Pemeriksaan Ossa Manus dengan Kualitas Citra Objektif dan Subjektif." Jurnal Kesehatan 12, no. 3 (2021): 359. http://dx.doi.org/10.26630/jk.v12i3.2653.

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In producing a good radiographic image, an optimization method is needed. This study was conducted to seek optimization of the radiographic examination of the manus ossa with objective and subjective image quality analysis. The research method is quantitative experimental, using a variety of exposure factors: 40kV 4 mAs, 40kV 10 mAs, 46 kV 5 mAs, 53 kV 2,5 mAs, 61kV 1,25 mAs. Then an objective quality analysis is carried out by measuring the pixels value, Signal to Noise Ratio (SNR), and the Exposure Index (EI) value as an indicator of exposure. For subjective image analysis with the assessment of image anatomy criteria using the method Visual Grading Analysis (VGA), then the test was carried out Wilcoxon to determine the relationship of respondents to VGA assessment. The results of the study obtained that the optimization method of the examination manus ossa at the exposure factor of 46 kV 5 mAs with the results of an objective image quality analysis of the range of pixel value 183,7 - 3, the SNR range of 12,2-1,77 while the subjective image quality analysis of the results VGA all images on a variety of exposure factors can be used in establishing a diagnosis. For the exposure indicator with the lowest EI at an exposure factor of 46 kV 5 mAs. The results of the Wilcoxon p-value&gt;0,05 so that there was no difference in the VGA value by 2 radiographers, therefore all image results on variations in exposure factors could be used in the radiographic examination of the ossa manus.

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Horgan, G. W., S. V. Murphy, and G. Simm. "Automatic assessment of sheep carcasses by image analysis." Animal Science 60, no. 2 (1995): 197–202. http://dx.doi.org/10.1017/s1357729800008341.

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AbstractThe commercial value of animal carcasses depends not only on their weight but also on their composition and shape (termed conformation). This is usually assessed subjectively by a skilled inspector. In this paper an attempt is described to assess the saleable meat yield of sheep carcasses by automatic digital image analysis. A low-cost system based on a still video camera and a personal computer was used. The results indicate that better prediction of saleable meat yield can be obtained using objective measures of carcass shape than from subjective conformation scores. Information from the intensities of colour components was not found to be useful, possibly due to difficulties with lighting and image quality. Recommendations are made for implementing a practical system.

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