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Journal articles on the topic 'Perceptual Quality Assessment'

Author: Grafiati

Published: 6 September 2023

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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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5

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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11

Wolfe, Virginia I., David P. Martin, and Chester I. Palmer. "Perception of Dysphonic Voice Quality by Naive Listeners." Journal of Speech, Language, and Hearing Research 43, no. 3 (2000): 697–705. http://dx.doi.org/10.1044/jslhr.4303.697.

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For clinical assessment as well as student training, there is a need for information pertaining to the perceptual dimensions of dysphonic voice. To this end, 24 naive listeners judged the similarity of 10 female and 10 male vowel samples, selected from within a narrow range of fundamental frequencies. Most of the perceptual variance for both sets of voices was associated with "degree of abnormality" as reflected by perceptual ratings as well as combined acoustic measures, based upon filtered and unfiltered signals. A second perceptual dimension for female voices was associated with high freque

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12

Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3457905.

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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low

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13

Lim, Jin-Young, Ho-Seok Chang, Dong-Wook Kang, Ki-Doo Kim, and Kyeong-Hoon Jung. "No-reference Perceptual Quality Assessment of Digital Image." Journal of Broadcast Engineering 13, no. 6 (2008): 849–58. http://dx.doi.org/10.5909/jbe.2008.13.6.849.

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14

Ma, Kede, Kai Zeng, and Zhou Wang. "Perceptual Quality Assessment for Multi-Exposure Image Fusion." IEEE Transactions on Image Processing 24, no. 11 (2015): 3345–56. http://dx.doi.org/10.1109/tip.2015.2442920.

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15

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

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Visual quality measure is one of the fundamental and important issues to numerous applications of image and video processing. In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. The proposed measure compares TVs between a distorted image and its reference image to represent the loss of image structural information. Because of the good performance of TV model in desc

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16

Dong, Xinghui, and Huiyu Zhou. "Texture synthesis quality assessment using perceptual texture similarity." Knowledge-Based Systems 194 (April 2020): 105591. http://dx.doi.org/10.1016/j.knosys.2020.105591.

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17

Yan, Weiqing, Guanghui Yue, Yuming Fang, Hua Chen, Chang Tang, and Gangyi Jiang. "Perceptual objective quality assessment of stereoscopic stitched images." Signal Processing 172 (July 2020): 107541. http://dx.doi.org/10.1016/j.sigpro.2020.107541.

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18

Sloan, Colm, Naomi Harte, Damien Kelly, Anil C. Kokaram, and Andrew Hines. "Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio." IEEE Transactions on Broadcasting 63, no. 4 (2017): 693–705. http://dx.doi.org/10.1109/tbc.2017.2704421.

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19

Chang, Hua-wen, Qiu-wen Zhang, Qing-gang Wu, and Yong Gan. "Perceptual image quality assessment by independent feature detector." Neurocomputing 151 (March 2015): 1142–52. http://dx.doi.org/10.1016/j.neucom.2014.04.081.

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20

Zhou Wang and Qiang Li. "Information Content Weighting for Perceptual Image Quality Assessment." IEEE Transactions on Image Processing 20, no. 5 (2011): 1185–98. http://dx.doi.org/10.1109/tip.2010.2092435.

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21

Chang, Hua-Wen, Hua Yang, Yong Gan, and Ming-Hui Wang. "Sparse Feature Fidelity for Perceptual Image Quality Assessment." IEEE Transactions on Image Processing 22, no. 10 (2013): 4007–18. http://dx.doi.org/10.1109/tip.2013.2266579.

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22

Tang, Lu, Chuangeng Tian, Leida Li, Bo Hu, Wei Yu, and Kai Xu. "Perceptual quality assessment for multimodal medical image fusion." Signal Processing: Image Communication 85 (July 2020): 115852. http://dx.doi.org/10.1016/j.image.2020.115852.

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23

Lowell, Soren Y. "The Acoustic Assessment of Voice in Continuous Speech." Perspectives on Voice and Voice Disorders 22, no. 2 (2012): 57–63. http://dx.doi.org/10.1044/vvd22.2.57.

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Acoustic measures are an essential component in the assessment of voice disorders, but the value of these measures is dependent on their relationship to perceptual voice quality and the degree to which these measures reflect the typical speaking patterns of the individual being assessed. Therefore, acoustic measures that can be accurately and reliably derived from continuous speech contexts, which are more representative of every day speaking patterns than sustained vowels, are fundamental to the assessment of voice. In this article, I review the current findings on acoustic measures that are

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24

Moorthy, A. K., and A. C. Bovik. "Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality." IEEE Transactions on Image Processing 20, no. 12 (2011): 3350–64. http://dx.doi.org/10.1109/tip.2011.2147325.

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25

Zhu, Hancheng, Yong Zhou, Zhiwen Shao, Wen-Liang Du, Jiaqi Zhao, and Rui Yao. "ARET-IQA: An Aspect-Ratio-Embedded Transformer for Image Quality Assessment." Electronics 11, no. 14 (2022): 2132. http://dx.doi.org/10.3390/electronics11142132.

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Image quality assessment (IQA) aims to automatically evaluate image perceptual quality by simulating the human visual system, which is an important research topic in the field of image processing and computer vision. Although existing deep-learning-based IQA models have achieved significant success, these IQA models usually require input images with a fixed size, which varies the perceptual quality of images. To this end, this paper proposes an aspect-ratio-embedded Transformer-based image quality assessment method, which can implant the adaptive aspect ratios of input images into the multihea

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26

Ahmed, Nisar, and Hafiz Muhammad Shahzad Asif. "Perceptual Quality Assessment of Digital Images Using Deep Features." Computing and Informatics 39, no. 3 (2020): 385–409. http://dx.doi.org/10.31577/cai_2020_3_385.

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27

Laparra, Valero, Johannes Ballé, Alexander Berardino, and Eero P. Simoncelli. "Perceptual image quality assessment using a normalized Laplacian pyramid." Electronic Imaging 2016, no. 16 (2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.16.hvei-103.

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28

Mu, Hao, and Woon_Seng Gan. "Perceptual Quality Improvement and Assessment for Virtual Bass Systems." Journal of the Audio Engineering Society 63, no. 11 (2015): 900–913. http://dx.doi.org/10.17743/jaes.2015.0079.

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29

Choi, Kang-Sun, Yeo-Min Yun, Jong-Woo Han, and Sung-Jea Ko. "8.4: Perceptual Quality Assessment for Motion Compensated Frame Interpolation." SID Symposium Digest of Technical Papers 41, no. 1 (2010): 102. http://dx.doi.org/10.1889/1.3499824.

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30

Kreiman, Jody, and Bruce R. Gerratt. "Perceptual Assessment of Voice Quality: Past, Present, and Future." Perspectives on Voice and Voice Disorders 20, no. 2 (2010): 62–67. http://dx.doi.org/10.1044/vvd20.2.62.

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Despite many years of research, we still do not know how to measure vocal quality. This paper reviews the history of quality assessment, describes some reasons why current approaches are unlikely to be fruitful, and proposes an alternative approach that addresses the primary difficulties with existing protocols.

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31

Winkler, Stefan. "Issues in vision modeling for perceptual video quality assessment." Signal Processing 78, no. 2 (1999): 231–52. http://dx.doi.org/10.1016/s0165-1684(99)00062-6.

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32

Liu, Min, Ke Gu, Guangtao Zhai, Patrick Le Callet, and Wenjun Zhang. "Perceptual Reduced-Reference Visual Quality Assessment for Contrast Alteration." IEEE Transactions on Broadcasting 63, no. 1 (2017): 71–81. http://dx.doi.org/10.1109/tbc.2016.2597545.

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33

Silva, Alessandro R., and Mylène C. Q. Farias. "Perceptual quality assessment of 3D videos with stereoscopic degradations." Multimedia Tools and Applications 79, no. 1-2 (2019): 1603–23. http://dx.doi.org/10.1007/s11042-019-08386-3.

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34

Yalman, Yildiray. "Histogram based perceptual quality assessment method for color images." Computer Standards & Interfaces 36, no. 6 (2014): 899–908. http://dx.doi.org/10.1016/j.csi.2014.04.002.

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35

Kuo, Wen-Hung, Po-Hung Lin, and Sheue-Ling Hwang. "A framework of perceptual quality assessment on LCD-TV." Displays 28, no. 1 (2007): 35–43. http://dx.doi.org/10.1016/j.displa.2006.11.005.

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36

Xia, Yingjie, Zhenguang Liu, Yan Yan, Yanxiang Chen, Luming Zhang, and Roger Zimmermann. "Media Quality Assessment by Perceptual Gaze-Shift Patterns Discovery." IEEE Transactions on Multimedia 19, no. 8 (2017): 1811–20. http://dx.doi.org/10.1109/tmm.2017.2679900.

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37

Mustafa, Safi, and Abdul Hameed. "Perceptual quality assessment of video using machine learning algorithm." Signal, Image and Video Processing 13, no. 8 (2019): 1495–502. http://dx.doi.org/10.1007/s11760-019-01494-5.

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38

Zhou, Wujie, Gangyi Jiang, and Mei Yu. "New visual perceptual pooling strategy for image quality assessment." Journal of Electronics (China) 29, no. 3-4 (2012): 254–61. http://dx.doi.org/10.1007/s11767-012-0818-7.

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39

Farnand, Susan, Young Jang, Lark Kwon Choi, and Chuck Han. "A methodology for perceptual image quality assessment of smartphone cameras – color quality." Electronic Imaging 2017, no. 12 (2017): 95–99. http://dx.doi.org/10.2352/issn.2470-1173.2017.12.iqsp-250.

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40

Silva, Maria Fabiana Bonfim de Lima, Sandra Madureira, Luiz Carlos Rusilo, and Zuleica Camargo. "Vocal quality assessment: methodological approach for a perceptive data analysis." Revista CEFAC 19, no. 6 (2017): 831–41. http://dx.doi.org/10.1590/1982-021620171961417.

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ABSTRACT Purpose: to present a methodological approach for interpreting perceptual judgments of vocal quality by a group of evaluators using the script Vocal Profile Analysis Scheme. Methods: a cross-sectional study based on 90 speech samples from 25 female teachers with voice disorders and/or laryngeal changes. Prior to the perceptual judgment, three perceptual tasks were performed to select samples to be presented to five evaluators using the Experiment script MFC 3.2 (software PRAAT). Next, a sequence of tests was applied, based on successive approaches of inter- and intra-evaluators’ behav

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41

Varga, Domonkos. "No-Reference Image Quality Assessment with Global Statistical Features." Journal of Imaging 7, no. 2 (2021): 29. http://dx.doi.org/10.3390/jimaging7020029.

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The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution

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42

Barsties v. Latoszek, Ben, Jörg Mayer, Christopher R. Watts, and Bernhard Lehnert. "Advances in Clinical Voice Quality Analysis with VOXplot." Journal of Clinical Medicine 12, no. 14 (2023): 4644. http://dx.doi.org/10.3390/jcm12144644.

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Background: The assessment of voice quality can be evaluated perceptually with standard clinical practice, also including acoustic evaluation of digital voice recordings to validate and further interpret perceptual judgments. The goal of the present study was to determine the strongest acoustic voice quality parameters for perceived hoarseness and breathiness when analyzing the sustained vowel [a:] using a new clinical acoustic tool, the VOXplot software. Methods: A total of 218 voice samples of individuals with and without voice disorders were applied to perceptual and acoustic analyses. Over

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43

Muschter, Evelyn, Andreas Noll, Jinting Zhao, et al. "Perceptual Quality Assessment of Compressed Vibrotactile Signals Through Comparative Judgment." IEEE Transactions on Haptics 14, no. 2 (2021): 291–96. http://dx.doi.org/10.1109/toh.2021.3077191.

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44

Sung, Jung-Min, Bong-Seok Choi, Bong-Yeol Choi, and Yeong-Ho Ha. "Perceptual Quality Assessment on Display based on Analytic Network Process." Journal of the Institute of Electronics and Information Engineers 51, no. 7 (2014): 180–89. http://dx.doi.org/10.5573/ieie.2014.51.7.180.

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45

Farnand, Susan, Young Jang, Chuck Han, and Hau Hwang. "A methodology for perceptual image quality assessment of smartphone cameras." Electronic Imaging 2016, no. 13 (2016): 1–5. http://dx.doi.org/10.2352/issn.2470-1173.2016.13.iqsp-202.

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46

Takam Tchendjou, Ghislain, and Emmanuel Simeu. "Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques." Sensors 22, no. 1 (2021): 175. http://dx.doi.org/10.3390/s22010175.

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This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main processes have been used to build our models: The first one uses deep learning with a convolutional neural network process, without any preprocessing. The second objective visual quality is computed by pooling several image features extracted from different concepts: the natural scene statistic in the spatial domain, the gradient magnitude, the Laplaci

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47

Batsi, Sophia, and Lisimachos P. Kondi. "Improved Temporal Pooling for Perceptual Video Quality Assessment Using VMAF." Electronic Imaging 2020, no. 11 (2020): 68–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-068.

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The Video Multimethod Assessment Fusion (VMAF) method, proposed by Netflix, offers an automated estimation of perceptual video quality for each frame of a video sequence. Then, the arithmetic mean of the per-frame quality measurements is taken by default, in order to obtain an estimate of the overall Quality of Experience (QoE) of the video sequence. In this paper, we validate the hypothesis that the arithmetic mean conceals the bad quality frames, leading to an overestimation of the provided quality. We also show that the Minkowski mean (appropriately parametrized) approximates well the subje

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48

Guangtao Zhai, Jianfei Cai, Weisi Lin, Xiaokang Yang, Wenjun Zhang, and M. Etoh. "Cross-Dimensional Perceptual Quality Assessment for Low Bit-Rate Videos." IEEE Transactions on Multimedia 10, no. 7 (2008): 1316–24. http://dx.doi.org/10.1109/tmm.2008.2004910.

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49

Wang, Shiqi, Ke Gu, Kai Zeng, Zhou Wang, and Weisi Lin. "Objective Quality Assessment and Perceptual Compression of Screen Content Images." IEEE Computer Graphics and Applications 38, no. 1 (2018): 47–58. http://dx.doi.org/10.1109/mcg.2016.46.

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50

Oh, J., S. I. Woolley, T. N. Arvanitis, and J. N. Townend. "A multistage perceptual quality assessment for compressed digital angiogram images." IEEE Transactions on Medical Imaging 20, no. 12 (2001): 1352–61. http://dx.doi.org/10.1109/42.974930.

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