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ITU Journal: ICT Discoveries, Vol. 3(1), June 2020

[20] V. Baroncini, “Results of the Subjective Testing of the [32] S. Becker, K.-R. Müller, T. Wiegand, and S. Bosse, “A
Responses to the Joint CfP on Video Compression Neural Network Model of Spatial Distortion Sensiti-
Technology with Capability beyond HEVC,” doc. vity for Video Quality Estimation,” in Proc. IEEE Int.
JVET-J0080, San Diego, CA, US, Apr. 2018. Workshop Machine Learning for Sig. Process. (MLSP),
Pittsburg, PA, US, pp.1–6, Oct. 2019.

st
[21] A. Valberg, Light Vision Color, 1 e., Wiley, Mar. 2005.
[33] M. Cheon and J. Lee, “Subjective and Objective Quali-
[22] M. H. Pinson and S. Wolf, “A New Standardized Me-
ty Assessment of Compressed 4K UHD Videos for
thod for Objectively Measuring Video Quality,” IEEE Immersive Experience,” IEEE Trans. Circuits Systems
Trans. Broadc., vol. 50, no. 3, pp. 312–322, Sep. 2004.
f. Video Technol., vol. 28, no. 7, pp. 1467–1480, 2018.
[23] M. Barkowsky, J. Bialkowski, B. Eskofier, R. Bitto, and
[34] K. Seshadrinatan, R. Soundararajan, A. C. Bovik, and
A. Kaup, “Temporal Trajectory Aware Video Quality
L. K. Cormack, “Study of Subjective and Objective
Measure,” IEEE J. Selected Topics in Sig. Process., vol. Quality Assessment of Video,” IEEE Trans. Image
3, no. 2, pp. 266–279, Apr. 2009.

Process., vol.19, no. 6, pp.1427–1441, June 2010.
[24] K. Seshadrinatan and A. C. Bovik, “Motion Tuned
[35] F. Zhang, S. Li, L. Ma, Y. C. Wong, and K. N. Ngan, “IVP
Spatio-Temporal Quality Assessment of Natural subjective quality video database,” 2011–2012, link:
Videos,” IEEE Trans. Image Process., vol.19, no. 2, pp. http://ivp.ee.cuhk.edu.hk/research/database/subjective.

335–350, Feb. 2010.

[36] M.Vranješ, S. Rimac-Drlje, and D.Vranješ, “ECVQ and
[25] W. Kim, J. Kim, S. Ahn, J. Kim, and S. Lee, “Deep video
EVVQ Video Quality Databases,” in Proc. IEEE Int.
quality assessor: From spatio-temporal visual sen- Symposium ELMAR, Zadar, HR, pp.13–17, Sep. 2012.

sitivity to a convolutional neural aggregation net-
work,” in Proc. 15 Europ. Conf. on Computer Vision [37] Y. Zhu, L. Song, R. Xie, and W. Zhang, “SJTU 4K Video
th

(ECCV), Munich, DE, pp. 219–234, Sep. 2018. Subjective Quality Dataset for Content Adaptive Bit-
rate Estimation without Encoding,” in Proc. IEEE Int.
[26] C. G. Bampis, Z. Li, and A. C. Bovik, “Spatiotemporal Symposium BMSB, Nara, JP, June 2016.
Feature Integration and Model Fusion for Full
Reference Video Quality Assessment,” IEEE Trans. [38] G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand,
Circuits Systems f. Video Technol., vol. 29, no. 8, pp. “Overview of the High Efficiency Video Coding (HE-
2256–2270, Aug. 2019. VC) Standard,” IEEE Trans. Circuits Systems f. Video

Technol., vol. 22, no.12, pp.1649–1668, Dec. 2012.
[27] D. McK. Kerslake, The Stress of Hot Environments, p.

st

37,1 e.,Cambridge University Press,July1972,link: [39] J. Pfaff et al., “Video Compression Using Generalized
https://books.google.de/books?id=FQo9AAAAIAAJ Binary Partitioning, Trellis Coded Quantization, Per-
&pg=PA37&f=false#v=snippet&q=%22square%20 ceptually Optimized Encoding, and Advanced Pre-
mean%20root%22&f=false. diction and Transform Coding,” IEEE Trans. Circuits
Systems f. Video Technol., vol. 30, no. 5, pp.1281–1295,

[28] ITU-R, Recommendation BT.500-13,“Method for the May 2020.
subjective assessment of the quality of television
pictures,” Geneva, CH, Jan. 2012. [40] JCT-VC and Fraunhofer HHI, “High Efficiency Video
Coding (HEVC),” link: https://hevc.hhi.fraunhofer.de.
[29] K. Pearson, “On Lines and Planes of Closest Fit to
Systems of Points in Space,” Philosoph. Mag., vol. 2, [41] JVET and Fraunhofer HHI, “VVCSoftware_VTM,” link:
no.11, pp. 559–572, 1901. https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware_VTM.

[30] J. L. Myers and A. D. Well, Research Design and Statis- [42] C. R. Helmrich, “ecodis – XPSNR – Information Page”,
tical Analysis, p. 508, 2 e., Lawrence Erlbaum, 2003. Mar. 2020, link: http://www.ecodis.de/xpsnr.htm.
nd
[31] ITU-T, Recommendation P.1401, “Methods, metrics [43] D. Kundu, D. Ghadiyaram, A. C. Bovik, and B. L. Evans,
and procedures for statistical evaluation, qualifica- “ESPL-Live HDR Image Quality Database,” May 2016,
tion and comparison of objective quality prediction link: http://live.ece.utexas.edu/research/HDRDB/hd
models,” Geneva, CH, Jan. 2020. r_index.html.

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