Academic literature on the topic 'Image quality assessment metric'

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Journal articles on the topic "Image quality assessment metric"

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Berezsky, O. M., M. O. Berezkyi, M. O. Dombrovskyi, P. B. Liashchynskyi, and G. M. Melnyk. "COMBINED METRIC FOR EVALUATING THE QUALITY OF SYNTHESIZED BIOMEDICAL IMAGES." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 168–81. https://doi.org/10.15588/1607-3274-2025-2-15.

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Context. This study addresses the problem of developing a new metric for evaluating the quality of synthesized images. The relevance of this problem is explained by the need for assessing the quality of artificially generated images. Additionally, the study highlights the potential of biomedical image synthesis based on diffusion models. The research results can be applied for biomedical image generation and quantitative quality assessment of synthesized images.Objective. The aim of this study is to develop a combined metric and an algorithm for biomedical image synthesis to assess the quality
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Bouida, Ahmed, Mustapha Khelifi, Mohammed Beladgham, and Fatima-Zohra Hamlili. "Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation." Traitement du Signal 38, no. 2 (2021): 281–89. http://dx.doi.org/10.18280/ts.380204.

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In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate
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Frackiewicz, Mariusz, Łukasz Machalica, and Henryk Palus. "New Combined Metric for Full-Reference Image Quality Assessment." Symmetry 16, no. 12 (2024): 1622. https://doi.org/10.3390/sym16121622.

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In recent years, many new metrics highly correlated with the Mean Opinion Score (MOS) have been proposed for assessing image quality through Full-Reference Image Quality Assessment (FR-IQA) methods, such as MDSI, HPSI, and GMSD. Eight of these selected metrics, which compare reference and distorted images in a symmetrical manner, are briefly described in this article, and their performance is evaluated using correlation criteria (PLCC, SROCC, and KROCC), as well as RMSE. The aim of this paper is to develop a new, efficient quality index based on a combination of several high-performance metric
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Hamoudi, Radhi Sh. "Study Algorithms which Assessed Quality of the Blurred Images." University of Thi-Qar Journal of Science 4, no. 4 (2014): 79–85. http://dx.doi.org/10.32792/utq/utjsci/v4i4.671.

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No-reference measurement of blurring artifacts in images is a difficult problem in image quality assessment field. In this paper, we present a no-reference blur metric to measure the quality of the images. These images are degraded using Gaussian blurring. Suggestion method depends on developing the Mean of Locally Standard deviation and Mean of the image (MLSD) model, this method is called Blur Quality Metric (BQM) and it calculates from numerical integral of the function in this model. And the BQM is compared with the No-reference Perceptual Blur Metrics (PBM) and the Entropy of the First De
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Berga, David, Pau Gallés, Katalin Takáts, et al. "QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution." Remote Sensing 15, no. 9 (2023): 2451. http://dx.doi.org/10.3390/rs15092451.

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The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able
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Besma, Sadou, Lahoulou Atidel, Bouden Toufik, R. Avila Anderson, H. Falk Tiago, and Akhtar Zahid. "Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 5 (2019): 1–14. https://doi.org/10.5281/zenodo.3531477.

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This paper focuses on no-reference image quality assessment(NR-IQA)metrics. In the literature, a wide range of algorithms are proposed to automatically estimate the perceived quality of visual data. However, most of them are not able to effectively quantify the various degradations and artifacts that the image may undergo. Thus, merging of diverse metrics operating in different information domains is hoped to yield better performances, which is the main theme of the proposed work. In particular, the metric proposed in this paper is based on three well-known NR-IQA objective metrics that depend
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Yu, Jianping, Gang Li, Shaohui Wang, and Ling Lin. "Image quality assessment metric for frame accumulated image." Review of Scientific Instruments 89, no. 1 (2018): 013703. http://dx.doi.org/10.1063/1.5020715.

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Okarma, Krzysztof. "Current Trends and Advances in Image Quality Assessment." Elektronika ir Elektrotechnika 25, no. 3 (2019): 77–84. http://dx.doi.org/10.5755/j01.eie.25.3.23681.

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Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used
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LU, WEN, LIHUO HE, WENJIAN TANG, FEI GAO, and WEILONG HOU. "A NOVEL COMPRESSED IMAGES QUALITY METRIC." International Journal of Image and Graphics 11, no. 02 (2011): 281–92. http://dx.doi.org/10.1142/s021946781100410x.

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As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the
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Sandić-Stanković, Dragana, Dragan Kukolj, and Patrick Le Callet. "Multi–Scale Synthesized View Assessment Based on Morphological Pyramids." Journal of Electrical Engineering 67, no. 1 (2016): 3–11. http://dx.doi.org/10.1515/jee-2016-0001.

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Abstract The Depth-Image-Based-Rendering (DIBR) algorithms used for 3D video applications introduce geometric distortions affecting the edge coherency in the synthesized images. In order to better deal with specific geometric distortions in the DIBR synthesized images, we propose full-reference metric based on multi-scale pyramid decompositions using morphological filters. The non-linear morphological filters used in multi-scale image decompositions maintain important geometric information such as edges across different resolution levels. We show that PSNR has particularly good agreement with
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Dissertations / Theses on the topic "Image quality assessment metric"

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Hettiarachchi, Don Lahiru Nirmal Manikka. "An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.

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Gao, Zhigang. "Image/video compression and quality assessment based on wavelet transform." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187195053.

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Preiss, Jens [Verfasser], Philipp [Akademischer Betreuer] Urban, Edgar [Akademischer Betreuer] Dörsam, and Michael [Akademischer Betreuer] Goesele. "Color-Image Quality Assessment: From Metric to Application / Jens Preiss. Betreuer: Philipp Urban ; Edgar Dörsam ; Michael Goesele." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2015. http://d-nb.info/1110980949/34.

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R, V. Krishnam Raju Kunadha Raju. "Perceptual Image Quality Prediction Using Region of Interest Based Reduced Reference Metrics Over Wireless Channel." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13631.

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As there is a rapid growth in the field of wireless communications, the demand for various multimedia services is also increasing. The data that is being transmitted suffers from distortions through source encoding and transmission over errorprone channels. Due to these errors, the quality of the content is degraded. There is a need for service providers to provide certain Quality of Experience (QoE) to the end user. Several methods are being developed by network providers for better QoE.The human tendency mainly focuses on distortions in the Region of Interest(ROI) which are perceived to be m
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MARINI, FABRIZIO. "Content based no-reference image quality metrics." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/29794.

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Images are playing a more and more important role in sharing, expressing, mining and exchanging information in our daily lives. Now we can all easily capture and share images anywhere and anytime. Since digital images are subject to a wide variety of distortions during acquisition, processing, compression, storage, transmission and reproduction; it becomes necessary to assess the Image Quality. In this thesis, starting from an organized overview of available Image Quality Assessment methods, some original contributions in the framework of No-reference image quality metrics are described.
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Alrished, Mohamad Ayad A. "A quantitative analysis and assessment of the performance of image quality metrics." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128987.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 79-82).<br>Image quality assessment addresses the distortion levels and the perceptual quality of a restored or corrupted image. A plethora of metrics has been developed to that end. The usual mean of success of an image quality metric is their ability to agree with the opinions of human subjects, often represented by the mean opinion score. Despite the promising performance of some image quality metrics
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Zhang, Zhengyu. "Quality Assessment of Light Field Images." Electronic Thesis or Diss., Rennes, INSA, 2024. http://www.theses.fr/2024ISAR0002.

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Les images de champs de lumière (LFI) suscitent un intérêt et une fascination remarquables en raison de leur importance croissante dans les applications immersives. Étant donné que les LFI peuvent être déformés à différentes étapes, de l'acquisition à la visualisation, l'évaluation de la qualité des images de champs de lumière (LFIQA) est d'une importance vitale pour surveiller les dégradations potentielles de la qualité des LFI.La première contribution (Chapitre 3) de ce travail se concentre sur le développement de deux métriques LFIQA sans référence (NR) fondées sur des caractéristiques ad-h
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CONDORELLI, FRANCESCA. "Photogrammetry and Deep Learning to improve Cultural Heritage records: extracting 3D metric information from historical images." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2914546.

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Zabaleta, Razquin Itziar. "Image processing algorithms as artistic tools in digital cinema." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672840.

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The industry of cinema has experienced a radical change in the last decades: the transition from film cinematography to its digital format. As a consequence, several challenges have appeared, but, at the same time, many possibilities are open now for cinematographers to explore with this new medium. In this thesis, we propose different tools that can be useful for cinematographers while doing their craft. First, we develop a tool for automatic color grading. It is a statistics-based method to automatically transfer the style from a graded image to unprocessed footage. Some advantages of th
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Sanja, Maksimović-Moićević. "Predlog nove mere za ocenu kvaliteta slike prilikom interpolacije i njena implementacija u računarskoj obradi signal slike." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=95429&source=NDLTD&language=en.

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<p>Osnovni doprinos ove doktorske disertacije je razvoj algortima i sistema za objektivnu procenu vizuelnog kvaliteta slike uzimajući u obzir najvažnija moguća oštećenja kao što su zamućenje ivica (oštrina) i poremećaj prirodnog izgleda teksture objekata na slici sa jedne strane i uticaj sadržaja slike (procenta ivica u slici) na procenu kvaliteta sa druge strane. Dakle, hipoteza izneta u ovom radu je da je potreban multiparametarski pristup da bi se dobila objektivna procena kvaliteta slike koja je što približnija subjektivnoj proceni.</p>
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Books on the topic "Image quality assessment metric"

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Wang, Zhou, and Alan C. Bovik. Modern Image Quality Assessment. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-02238-8.

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Ding, Yong, and Guangming Sun. Stereoscopic Image Quality Assessment. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7764-2.

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Bigand, André, Julien Dehos, Christophe Renaud, and Joseph Constantin. Image Quality Assessment of Computer-generated Images. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73543-6.

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International Commission on Radiation Units and Measurements., ed. Medical imaging--the assessment of image quality. International Commission on Radiation Units and Measurements, 1995.

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Ding, Yong. Visual Quality Assessment for Natural and Medical Image. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56497-4.

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Evans, Ronald J. Image quality metrics and application of the Square Root Integral (SQRI) metric: An overview. Air Force Human Resources Laboratory, Air Force Systems Command, 1990.

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Zhu, Shu-Yu. Perceptual wavelet coding and quality assessment for still image. National Library of Canada, 2000.

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United States. National Telecommunications and Information Administration, ed. Features for automated quality assessment of digitally transmitted video. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1990.

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Wiseman, Chad D. Multi-metric index development for biological monitoring in Washington State streams. 2nd ed. Washington State Dept. of Ecology, 2003.

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Wiseman, Chad D. Multi-metric index development for biological monitoring in Washington State streams. 2nd ed. Washington State Dept. of Ecology, 2003.

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Book chapters on the topic "Image quality assessment metric"

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Piórkowski, Rafał, and Radosław Mantiuk. "Reliability of Local Ground Truth Data for Image Quality Metric Assessment." In Image Processing and Communications Challenges 10. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03658-4_5.

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Okarma, Krzysztof. "Colour Image Quality Assessment Using the Combined Full-Reference Metric." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_30.

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Kaur, Roopdeep, and Gour Karmakar. "Image Quality Assessment Metric Fusing Traditional and Dempster-Shafer Theory." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_37.

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Yao, Shunyu, Yue Cao, Yabo Zhang, and Wangmeng Zuo. "Misalignment Insensitive Perceptual Metric for Full Reference Image Quality Assessment." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8552-4_35.

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Charrier, Christophe, Abdelhakim Saadane, and Christine Fernandez-Maloigne. "No-Reference Learning-Based and Human Visual-Based Image Quality Assessment Metric." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_23.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Quantitative Metrics for Mammographic Image Quality Assessment." In Non-Linear Filters for Mammogram Enhancement. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_10.

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Okarma, Krzysztof. "Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment." In Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13208-7_67.

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Romani, Eduardo, Wyllian Bezerra da Silva, Keiko Verônica Ono Fonseca, Dubravko Culibrk, and Alexandre de Almeida Prado Pohl. "Full-Reference SSIM Metric for Video Quality Assessment with Saliency-Based Features." In New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23222-5_66.

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Yang, Luyu, Jiang Liu, and Chenqiang Gao. "An Error-Activation-Guided Blind Metric for Stitched Panoramic Image Quality Assessment." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7302-1_22.

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Okarma, Krzysztof. "A Validation of Combined Metrics for Color Image Quality Assessment." In Computer Vision and Graphics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11331-9_1.

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Conference papers on the topic "Image quality assessment metric"

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Sharma, Divya. "Image Quality Assessment Metrics for Hyperspectral Image Compression Algorithms." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877577.

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Muhamediyeva, Dilnoz, Boymirzo Samijonov, Ubaydullakhan Ganiyev, Ilhomjon Valijonov, and Bobur Jumayev. "Image quality assessment based on metrics using quantum algorithms." In Fourth International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2025), edited by Arthur Gibadullin and Khamza Eshankulov. SPIE, 2025. https://doi.org/10.1117/12.3072742.

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Safonov, Nickolay, Dmitriy Kulikov, and Dmitriy Vatolin. "Multi-Screen Effects on Quality Assessment: Investigating Banding Metrics Inconsistencies." In 2024 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2024. https://doi.org/10.1109/vcip63160.2024.10849889.

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Zhou, Qiangqiang, Xianhui Liu, Lin Zhang, Weidong Zhao, and Yufei Chen. "Saliency-based image quality assessment metric." In 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE, 2016. http://dx.doi.org/10.1109/icsai.2016.7811082.

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Xu, Jingtao, Qiaohong Li, Peng Ye, Haiqing Du, and Yong Liu. "Statistical metric fusion for image quality assessment." In 2014 Visual Communications and Image Processing (VCIP). IEEE, 2014. http://dx.doi.org/10.1109/vcip.2014.7051522.

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Zhai, Guangtao, Wenjun Zhang, Yi Xu, and Weisi Lin. "LGPS: Phase Based Image Quality Assessment Metric." In 2007 IEEE Workshop on Signal Processing Systems. IEEE, 2007. http://dx.doi.org/10.1109/sips.2007.4387618.

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Wen Lu, Xinbo Gao, Xuelong Li, and Dacheng Tao. "An image quality assessment metric based contourlet." In 2008 15th IEEE International Conference on Image Processing - ICIP 2008. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4711969.

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Zhou, Xuemei, Evangelos Alexiou, Irene Viola, and Pablo Cesar. "PointPCA+: Extending PointPCA Objective Quality Assessment Metric." In 2023 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW). IEEE, 2023. http://dx.doi.org/10.1109/icipc59416.2023.10328338.

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Stojanovic, Nenad, Boban Bondzulic, Boban Pavlovic, and Vladimir Ristic. "Contrast Quality Measure: Full-Reference Image Quality Assessment Metric for Infrared Images." In 2022 New Trends in Signal Processing (NTSP). IEEE, 2022. http://dx.doi.org/10.23919/ntsp54843.2022.9920403.

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Peng Zhang, Wengang Zhou, Lei Wu, and Houqiang Li. "SOM: Semantic obviousness metric for image quality assessment." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298853.

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Reports on the topic "Image quality assessment metric"

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Schoening, T., and J. Greinert. Quality of image-based manganese nodule abundance assessment. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305928.

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Yang, Joyce. Face Analysis Technology Evaluation (FATE) Face Image Quality Vector Assessment:. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8485.

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Beauchemin, M., K. B. Fung, and X. Geng. A Method Based on Local Variance for Quality Assessment of Multiresolution Image Fusion. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219921.

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Allada, Venkata Bhanu Chowdary. Implementation of image quality assessment algorithms for descriptive statistics and deep learning on StegoAppDB. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-878.

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Sabatino, Ester, Edouard Simon, Fara Breuer, and JUliette Renaut. Developing Assessment Criteria for Defence Cooperation. EsadeGeo. Center for Global Economy and Geopolitics, 2022. https://doi.org/10.56269/202206es.

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This working paper set out on the ambitious task of creating a tool to enable consistent and high-quality evaluation of defence cooperation in the EU framework and among EU Member States at four different levels (i.e. politico-strategic, operational, capability development and partnership). Indicators were developed to assess the effectiveness, efficiency, coherence and sustainability of defence cooperation at these four levels. Each metric is judged against parameters ranging from 0 to 4 (with 4 representing the best possible outcome). While this working paper is relevant to users aiming at e
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Small Lung Nodule Volume Assessment and Monitoring in Low Dose CT Screening, Clinically Feasible Profile. Chair Artit Jirapatnakul, James Mulshine, and Kyle Myers. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20231219.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Profile Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) defines the technical methods to be used for evaluating conformance with profile requirements. This includes the steps needed for clinical sites and equipment vendors to be compliant with the Profile. This QIBA Profile (Sma
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Montagna, Tea', Luis De Leo'n, Nina McDonnell, et al. Natural resource assessment of the Barrett?s Farm Unit: Minute Man National Historical Park. National Park Service, 2024. http://dx.doi.org/10.36967/2305887.

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This study used a combination of field surveys, historical data, literature review, and formal methods to assess the biodiversity and habitat integrity of vertebrates, excluding mammals at Barrett?s Farm unit (BFU). A one season full inventory of the herpetofauna was completed in 2023. The herpetological survey documented ten species commonly found in Massachusetts (4 frogs, 1 toad, 2 salamanders, 1 snake and 2 turtles), with only one species (Lithobates pipiens, Leopard Frog) being seen in large numbers. Several species (Ambystoma maculatum, Spotted Salamander; Dryophytes versicolor, Gray Tre
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Jones, David, Roy Cook, John Sovell, et al. Natural resource condition assessment: Lincoln Boyhood National Memorial. National Park Service, 2024. http://dx.doi.org/10.36967/2301822.

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The National Park Service (NPS) Natural Resource Condition Assessment (NRCA) Program administered by the NPS Water Resources Division evaluates current conditions for important natural resources and resource indicators using primarily existing information and data. NRCAs also report on trends in resource condition, when possible, identify critical data gaps, and characterize a general level of confidence for study findings. This NRCA complements previous scientific endeavors, is multi-disciplinary in scope, employs a hierarchical indicator framework, identifies and develops reference condition
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Mathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, et al. Development of Latitude/Longitude (and Route/Milepost) Model for Positioning Traffic Management Cameras. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317720.

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Abstract:
Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras an
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CT Lung Densitometry, Consensus QIBA Profile. Chair Charles Hatt and Miranda Kirby. • The Publisher is Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20200904.

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The goal of a QIBA Profile is to achieve a repeatable and useful level of performance for measures of lung density from quantitative CT using the RA-950 HU and Perc15 biomarkers of emphysema. Please see Appendix C for more detailed information on the calculation of and rationale for RA-950 HU and Perc15 as the biomarkers of choice. The Claim (Section 2) describes the performance in terms of bias and precision of RA-950 HU and Perc15 for detecting change in lung density. The Activities (Section 3) describe how to generate RA-950 HU and Perc15 for longitudinal studies of the change in lung densi
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