Relevant bibliographies by topics / Perceptual Quality Assessment

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  1. Journal articles

Academic literature on the topic 'Perceptual Quality Assessment'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Perceptual Quality Assessment"

1

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 cons
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2

Da, 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.

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3

Hamberg, Roelof, and Huib de Ridder. "Continuous assessment of perceptual image quality." Journal of the Optical Society of America A 12, no. 12 (1995): 2573. http://dx.doi.org/10.1364/josaa.12.002573.

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4

Wang, Yinan, Andrei Chubarau, Hyunjin Yoo, Tara Akhavan, and James Clark. "Age-specific perceptual image quality assessment." Electronic Imaging 35, no. 8 (2023): 302–1. http://dx.doi.org/10.2352/ei.2023.35.8.iqsp-302.

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Elloumi, 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 (2017): 63–80. http://dx.doi.org/10.4018/ijssmet.2017010105.

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A 3D mesh can be subjected to different types of operations, such as compression, watermarking etc. Such processes lead to geometric distortions compared to the original version. In this context, quantifying the resultant modifications to the original mesh and evaluating the perceptual quality of degraded meshes become a critical issue. The perceptual 3D meshes quality is central in several applications to preserve the visual appearance of these treatments. The used metrics results have to be well correlated to the visual perception of humans. Although there are objective metrics, they do not
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6

Yang, Huan, Yuming Fang, and Weisi Lin. "Perceptual Quality Assessment of Screen Content Images." IEEE Transactions on Image Processing 24, no. 11 (2015): 4408–21. http://dx.doi.org/10.1109/tip.2015.2465145.

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7

Agudelo-Medina, Oscar A., Hernan Dario Benitez-Restrepo, Gemine Vivone, and Alan Bovik. "Perceptual Quality Assessment of Pan-Sharpened Images." Remote Sensing 11, no. 7 (2019): 877. http://dx.doi.org/10.3390/rs11070877.

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Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused products whose subjectivity renders the assessment of pansharpened data a challenging problem. Most previous research on the development of PS algorithms has only superficially addressed the issue of qualitative evaluation, generally by depicting visual representations of the fused images. Hence, it is highly desirable to be able to predict pan-sharp
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8

Hu, 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 (2013): 8764–88. http://dx.doi.org/10.1080/01431161.2013.846488.

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9

Chan, 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.

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10

Shahriari, Y., Q. Ding, R. Fidler, et al. "Perceptual Image Processing Based Ecg Quality Assessment." Journal of Electrocardiology 49, no. 6 (2016): 937. http://dx.doi.org/10.1016/j.jelectrocard.2016.09.040.

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