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Статті в журналах з теми "Perceptual Quality Assessment"
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 (June 28, 2022): 580–88. http://dx.doi.org/10.1609/aaai.v36i1.19937.
Повний текст джерелаDa, Pan, GuiYing Song, Ping Shi, and HaoCheng Zhang. "Perceptual quality assessment of nighttime video." Displays 70 (December 2021): 102092. http://dx.doi.org/10.1016/j.displa.2021.102092.
Повний текст джерелаHamberg, Roelof, and Huib de Ridder. "Continuous assessment of perceptual image quality." Journal of the Optical Society of America A 12, no. 12 (December 1, 1995): 2573. http://dx.doi.org/10.1364/josaa.12.002573.
Повний текст джерелаWang, Yinan, Andrei Chubarau, Hyunjin Yoo, Tara Akhavan, and James Clark. "Age-specific perceptual image quality assessment." Electronic Imaging 35, no. 8 (January 16, 2023): 302–1. http://dx.doi.org/10.2352/ei.2023.35.8.iqsp-302.
Повний текст джерелаElloumi, Nessrine, Habiba Loukil Hadj Kacem, Nilanjan Dey, Amira S. Ashour, and Med Salim Bouhlel. "Perceptual Metrics Quality." International Journal of Service Science, Management, Engineering, and Technology 8, no. 1 (January 2017): 63–80. http://dx.doi.org/10.4018/ijssmet.2017010105.
Повний текст джерелаYang, Huan, Yuming Fang, and Weisi Lin. "Perceptual Quality Assessment of Screen Content Images." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 4408–21. http://dx.doi.org/10.1109/tip.2015.2465145.
Повний текст джерелаAgudelo-Medina, Oscar A., Hernan Dario Benitez-Restrepo, Gemine Vivone, and Alan Bovik. "Perceptual Quality Assessment of Pan-Sharpened Images." Remote Sensing 11, no. 7 (April 11, 2019): 877. http://dx.doi.org/10.3390/rs11070877.
Повний текст джерелаHu, Anzhou, Rong Zhang, Dong Yin, Yuan Chen, and Xin Zhan. "Perceptual quality assessment of SAR image compression." International Journal of Remote Sensing 34, no. 24 (October 24, 2013): 8764–88. http://dx.doi.org/10.1080/01431161.2013.846488.
Повний текст джерелаChan, Kit Yan, and Ulrich Engelke. "Fuzzy regression for perceptual image quality assessment." Engineering Applications of Artificial Intelligence 43 (August 2015): 102–10. http://dx.doi.org/10.1016/j.engappai.2015.04.007.
Повний текст джерелаShahriari, Y., Q. Ding, R. Fidler, M. Pelter, Y. Bai, A. Villaroman, and X. Hu. "Perceptual Image Processing Based Ecg Quality Assessment." Journal of Electrocardiology 49, no. 6 (November 2016): 937. http://dx.doi.org/10.1016/j.jelectrocard.2016.09.040.
Повний текст джерелаДисертації з теми "Perceptual Quality Assessment"
Dhakal, Prabesh, Prabhat Tiwari, and Pawan Chan. "Perceptual Video Quality Assessment Tool." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2576.
Повний текст джерелаIn our research work, we have designed the tool that can be used to conduct a mass-scale level survey or subjective tests. ACR is the only method used to carry out the subjective video assessment. The test is very useful in the context of a video streaming quality. The survey can be used in various countries and sectors with low internet speeds to determine the kind of video or the compression technique, bit rate, or format that gives the best quality.
0700627491, 0760935352
Yang, Kai-Chieh. "Perceptual quality assessment for compressed video." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3284171.
Повний текст джерелаTitle from first page of PDF file (viewed Mar. 14, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 149-156).
Rix, Antony W. "Perceptual techniques in audio quality assessment." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/14286.
Повний текст джерелаSavvides, Vasos E. "Perceptual models in speech quality assessment and coding." Thesis, Loughborough University, 1988. https://dspace.lboro.ac.uk/2134/36273.
Повний текст джерелаZhu, Shu-Yu. "Perceptual wavelet coding and quality assessment for still image." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/MQ53450.pdf.
Повний текст джерелаHuynh-Thu, Quan. "Perceptual quality assessment of communications-grade video with temporal artefacts." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502128.
Повний текст джерелаOsberger, Wilfried. "Perceptual vision models for picture quality assessment and compression applications." Thesis, Queensland University of Technology, 1999.
Знайти повний текст джерелаGuarnieri, Gabriele. "High dynamic range images: processing, display and perceptual quality assessment." Doctoral thesis, Università degli studi di Trieste, 2009. http://hdl.handle.net/10077/3121.
Повний текст джерелаThe intensity of natural light can span over 10 orders of magnitude from starlight to direct sunlight. Even in a single scene, the luminance of the bright areas can be thousands or millions of times greater than the luminance in the dark areas; the ratio between the maximum and the minimum luminance values is commonly known as dynamic range or contrast. The human visual system is able to operate in an extremely wide range of luminance conditions without saturation and at the same time it can perceive fine details which involve small luminance differences. Our eyes achieve this ability by modulating their response as a function of the local mean luminance with a process known as local adaptation. In particular, the visual sensation is not linked to the absolute luminance, but rather to its spatial and temporal variation. One consequence of the local adaptation capability of the eye is that the objects in a scene maintain their appearance even if the light source illuminating the scene changes significantly. On the other hand, the technologies used for the acquisition and reproduction of digital images are able to handle correctly a significantly smaller luminance range of 2 to 3 orders of magnitude at most. Therefore, a high dynamic range (HDR) image poses several challenges and requires the use of appropriate techniques. These elementary observations define the context in which the entire research work described in this Thesis has been performed. As indicated below, different fields have been considered; they range from the acquisition of HDR images to their display, from visual quality evaluation to medical applications, and include some developments on a recently proposed class of display equipment. An HDR image can be captured by taking multiple photographs with different exposure times or by using high dynamic range sensors; moreover, synthetic HDR images can be generated with computer graphics by means of physically-based algorithms which often involve advanced lighting simulations. An HDR image, although acquired correctly, can not be displayed on a conventional monitor. The white level of most devices is limited to a few hundred cd/m² by technological constraints, primarily linked to the power consumption and heat dissipation; the black level also has a non negligible luminance, in particular for devices based on the liquid crystal technology. However, thanks to the aforementioned properties of the human visual system, an exact reproduction of the luminance in the original scene is not strictly necessary in order to produce a similar sensation in the observer. For this purpose, dynamic range reduction algorithms have been developed which attenuate the large luminance variations in an image while preserving as far as possible the fine details. The most simple dynamic range reduction algorithms map each pixel individually with the same nonlinear function commonly known as tone mapping curve. One operator we propose, based on a modified logarithmic function, has a low computational cost and contains one single user-adjustable parameter. However, the methods belonging to this category can reduce the visibility of the details in some portions of the image. More advanced methods also take into account the pixel neighborhood. This approach can achieve a better preservation of the details, but the loss of one-to-one mapping from input luminances to display values can lead to the formation of gradient reversal effects, which typically appear as halos around the object boundaries. Different solutions to this problem have been attempted. One method we introduce is able to avoid the formation of halos and intrinsically prevents any clipping of the output display values. The method is formulated as a constrained optimization problem, which is solved efficiently by means of appropriate numerical methods. In specific applications, such as the medical one, the use of dynamic range reduction algorithms is discouraged because any artifacts introduced by the processing can lead to an incorrect diagnosis. In particular, a one-to-one mapping from the physical data (for instance, a tissue density in radiographic techniques) to the display value is often an essential requirement. For this purpose, high dynamic range displays, capable of reproducing images with a wide luminance range and possibly a higher bit depth, are under active development. Dual layer LCD displays, for instance, use two liquid crystal panels stacked one on top of the other over an enhanced backlight unit in order to achieve a dynamic range of 4 ÷ 5 orders of magnitude. The grayscale reproduction accuracy is also increased, although a “bit depth” can not be defined unambiguously because the luminance levels obtained by the combination of the two panels are partially overlapped and unevenly spaced. A dual layer LCD display, however, requires the use of complex splitting algorithms in order to generate the two images which drive the two liquid crystal panels. A splitting algorithm should compensate multiple sources of error, including the parallax introduced by the viewing angle, the gray-level clipping introduced by the limited dynamic range of the panels, the visibility of the reconstruction error, and glare effects introduced by an unwanted light scattering between the two panels. For these reasons, complex constrained optimization techniques are necessary. We propose an objective function which incorporates all the desired constraints and requirements and can be minimized efficiently by means of appropriate techniques based on multigrid methods. The quality assessment of high dynamic range images requires the development of appropriate techniques. By their own nature, dynamic range reduction algorithms change the luminance values of an image significantly and make most image fidelity metrics inapplicable. Some particular aspects of the methods can be quantified by means of appropriate operators; for instance, we introduce an expression which describes the detail attenuation introduced by a tone mapping curve. In general, a subjective quality assessment is preferably performed by means of appropriate psychophysical experiments. We conducted a set of experiments, targeted specifically at measuring the level of agreement between different users when adjusting the parameter of the modified logarithmic mapping method we propose. The experimental results show a strong correlation between the user-adjusted parameter and the image statistics, and suggest a simple technique for the automatic adjustment of this parameter. On the other hand, the quality assessment in the medical field is preferably performed by means of objective methods. In particular, task-based quality measures evaluate by means of appropriate observer studies the clinical validity of the image used to perform a specific diagnostic task. We conducted a set of observer studies following this approach, targeted specifically at measuring the clinical benefit introduced by a high dynamic range display based on the dual layer LCD technology over a conventional display with a low dynamic range and 8-bit quantization. Observer studies are often time consuming and difficult to organize; in order to increase the number of tests, the human observers can be partially replaced by appropriate software applications, known as model observers or computational observers, which simulate the diagnostic task by means of statistical classification techniques. This thesis is structured as follows. Chapter 1 contains a brief background of concepts related to the physiology of human vision and to the electronic reproduction of images. The description we make is by no means complete and is only intended to introduce some concepts which will be extensively used in the following. Chapter 2 describes the technique of high dynamic range image acquisition by means of multiple exposures. In Chapter 3 we introduce the dynamic range reduction algorithms, providing an overview of the state of the art and proposing some improvements and novel techniques. In Chapter 4 we address the topic of quality assessment in dynamic range reduction algorithms; in particular, we introduce an operator which describes the detail attenuation introduced by tone mapping curves and describe a set of psychophysical experiments we conducted for the adjustment of the parameter in the modified logarithmic mapping method we propose. In Chapter 5 we move to the topic of medical images and describe the techniques used to map the density data of radiographic images to display luminances. We point out some limitations of the current technical recommendation and propose an improvement. In Chapter 6 we describe in detail the dual layer LCD prototype and propose different splitting algorithms for the generation of the two images which drive the two liquid crystal panels. In Chapter 7 we propose one possible technique for the estimation of the equivalent bit depth of a dual layer LCD display, based on a statistical analysis of the quantization noise. Finally, in Chapter 8 we address the topic of objective quality assessment in medical images and describe a set of observer studies we conducted in order to quantify the clinical benefit introduced by a high dynamic range display. No general conclusions are offered; the breadth of the subjects has suggested to draw more focused comments at the end of the individual chapters.
XXI Ciclo
1982
Oh, Joonmi. "Human visual system informed perceptual quality assessment models for compressed medical images." Thesis, University of Birmingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368425.
Повний текст джерелаChintala, Bala Venkata Sai Sundeep. "Objective Perceptual Quality Assessment of JPEG2000 Image Coding Format Over Wireless Channel." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17785.
Повний текст джерелаКниги з теми "Perceptual Quality Assessment"
Zhu, Shu-Yu. Perceptual wavelet coding and quality assessment for still image. Ottawa: National Library of Canada, 2000.
Знайти повний текст джерелаOh, Joonmi. Human visual system informed perceptual quality assessment models for compressed medical images. Birmingham: University of Birmingham, 2000.
Знайти повний текст джерелаZhai, Guangtao, Vinit Jakhetiya, Ke Gu, Lu Zhang, and Xiongkuo Min, eds. Computational Neuroscience for Perceptual Quality Assessment. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-950-8.
Повний текст джерелаSavvides, Vassos E. Perceptual models in speech quality assessment and coding. 1988.
Знайти повний текст джерелаHinterleitner, Florian. Quality of Synthetic Speech: Perceptual Dimensions, Influencing Factors, and Instrumental Assessment. Springer, 2017.
Знайти повний текст джерелаHinterleitner, Florian. Quality of Synthetic Speech: Perceptual Dimensions, Influencing Factors, and Instrumental Assessment. Springer, 2017.
Знайти повний текст джерелаHinterleitner, Florian. Quality of Synthetic Speech: Perceptual Dimensions, Influencing Factors, and Instrumental Assessment. Springer, 2018.
Знайти повний текст джерелаLeon, Susan A., Amy D. Rodriguez, and John C. Rosenbek. Right Hemisphere Damage and Prosody. Edited by Anastasia M. Raymer and Leslie J. Gonzalez Rothi. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199772391.013.15.
Повний текст джерелаЧастини книг з теми "Perceptual Quality Assessment"
Guan, Mengda, Yuanyuan Lyu, Wanyue Cao, Xingwang Wu, Jingjing Lu, and S. Kevin Zhou. "Perceptual Quality Assessment of Chest Radiograph." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 315–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87234-2_30.
Повний текст джерелаChen, Zidong, and Xiongkuo Min. "Perceptual Quality Assessment of TTS-Synthesized Speech." In Communications in Computer and Information Science, 423–35. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0856-1_31.
Повний текст джерелаEkmekcioglu, Erhan, Stewart Worrall, Demuni De Silva, Anil Fernando, and Ahmet M. Kondoz. "Depth Based Perceptual Quality Assessment for Synthesised Camera Viewpoints." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 76–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35145-7_10.
Повний текст джерелаLi, CongLi, Chao Xu, ChaoYi Chen, ChengJun Xu, and Zhe Wei. "Blind Perceptual Quality Assessment for Single Image Motion Deblurring." In Biometric Recognition, 531–40. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_54.
Повний текст джерелаLi, Leida, Bo Hu, Yipo Huang, and Hancheng Zhu. "Reduced-reference Perceptual Discrepancy Learning for Image Restoration Quality Assessment." In Artificial Intelligence, 359–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_31.
Повний текст джерелаJinjin, Gu, Cai Haoming, Chen Haoyu, Ye Xiaoxing, Jimmy S. Ren, and Dong Chao. "PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration." In Computer Vision – ECCV 2020, 633–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58621-8_37.
Повний текст джерелаYu, Yanwei, Zhengding Lu, Hefei Ling, and Fuhao Zou. "No-Reference Perceptual Quality Assessment of JPEG Images Using General Regression Neural Network." In Advances in Neural Networks - ISNN 2006, 638–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_94.
Повний текст джерелаLiu, Hantao, and Zhou Wang. "Perceptual Quality Assessment of Medical Images." In Encyclopedia of Biomedical Engineering, 588–96. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-801238-3.64099-0.
Повний текст джерелаWang, Zhou, Alan C. Bovik, and Hamid R. Sheikh. "Structural Similarity Based Image Quality Assessment." In Digital Video Image Quality and Perceptual Coding, 225–42. CRC Press, 2017. http://dx.doi.org/10.1201/9781420027822-7.
Повний текст джерелаVenkata Rao, D., and L. Pratap Reddy. "Image Quality Assessment Based on Weighted Perceptual Features." In Machine Interpretation of Patterns, 29–55. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814299190_0002.
Повний текст джерелаТези доповідей конференцій з теми "Perceptual Quality Assessment"
Avanaki, Ali R. N., Kathryn Espig, Albert Xthona, Daren Brooks, John Young, and Tom Kimpe. "Perceptual image quality in digital dermoscopy." In Image Perception, Observer Performance, and Technology Assessment, edited by Frank W. Samuelson and Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2549880.
Повний текст джерелаFang, Yuming, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. "Perceptual Quality Assessment of Smartphone Photography." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00373.
Повний текст джерелаCheon, Manri, Sung-Jun Yoon, Byungyeon Kang, and Junwoo Lee. "Perceptual Image Quality Assessment with Transformers." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00054.
Повний текст джерелаXu, Jiahua, Jing Li, Xingguang Zhou, Wei Zhou, Baichao Wang, and Zhibo Chen. "Perceptual Quality Assessment of Internet Videos." In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3475486.
Повний текст джерелаDuan, Huiyu, Guangtao Zhai, Xiongkuo Min, Yucheng Zhu, Yi Fang, and Xiaokang Yang. "Perceptual Quality Assessment of Omnidirectional Images." In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018. http://dx.doi.org/10.1109/iscas.2018.8351786.
Повний текст джерелаKhatami, Alireza, and Ahmadh Mahmoudi-Aznaveh. "Deep perceptual similarity and Quality Assessment." In 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2023. http://dx.doi.org/10.1109/ipria59240.2023.10147170.
Повний текст джерелаJoveluro, P., H. Malekmohamadi, W. A. C. Fernando, and A. M. Kondoz. "Perceptual Video Quality Metric for 3D video quality assessment." In 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON 2010). IEEE, 2010. http://dx.doi.org/10.1109/3dtv.2010.5506331.
Повний текст джерелаSeshadrinathan, Kalpana, and Alan C. Bovik. "Motion-based perceptual quality assessment of video." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2009. http://dx.doi.org/10.1117/12.811817.
Повний текст джерелаMier, Juan Carlos, Eddie Huang, Hossein Talebi, Feng Yang, and Peyman Milanfar. "Deep Perceptual Image Quality Assessment for Compression." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506217.
Повний текст джерелаFarid, Muhammad Shahid, Maurizio Lucenteforte, and Marco Grangetto. "Perceptual quality assessment of 3D synthesized images." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019307.
Повний текст джерела