Deep learning based combating strategy for COVID19 induced increased video consumption

Deep learning based combating strategy for COVID-19 induced increased video consumption

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-7
Year of Publication : 2020
Authors : Sangeeta, Preeti Gulia
DOI :  10.14445/22315381/IJETT-V68I7P212S

Citation 

MLA Style: Sangeeta, Preeti Gulia  "Deep learning based combating strategy for COVID-19 induced increased video consumption" International Journal of Engineering Trends and Technology 68.7(2020):78-82. 

APA Style:Sangeeta, Preeti Gulia. Deep learning based combating strategy for COVID-19 induced increased video consumption International Journal of Engineering Trends and Technology, 68(7),78-82.

Abstract
COVID-19 epidemic has brought tremendous changes globally. The adoption of prevention strategies like lockdown and remote working has suddenly changed all aspects of human life. A surge shift in online mode has been observed and it tremendously increased the internet traffic with a suitable rise in video content. Research and surveys state that these changes will become new normal and will last for a long time. As the bandwidths are limited and cannot be expanded instantly, there arose a need for alternate techniques to be explored to deal with growing video content efficiently. The lightweight and powerful deep learning based video compression and analytics techniques may help in efficiently processing video content. Deep learning based techniques are already giving potent results both in the video compression and video analytics domain independently. In this paper, the accelerated impact of COVID-19 on video compression methods has been demonstrated and proposed joint video compression-cum-analytics scheme which may significantly provide fast and efficient video analytics from the compressed video optimizing whole network.

Reference

[1] BCG Covid-19 consumer sentiment research India Survey Snapshot March 23-26 results
[2] https://www.nielsen.com/apac/en/insights/report/2020/theimpact- of-covid-19-on-media-consumption-across-northasia/
[3] https://www.hindustantimes.com/tech/covid-19-lockdowndemand- for-netflix-whatsapp-zoom-and-other-ott-serviceswitness- major-growth-among-indians/story- KW0CBfBzCGnJJioTvowpnJ.html
[4] C. V. networking Index. Forecast and methodology, 2016- 2021, white paper. San Jose, CA, USA, 1, 2016.
[5] C.-Y. Wu, M. Zaheer, H. Hu, R. Manmatha, A. J. Smola, and P. Kr¨ahenb¨uhl. “Compressed video action recognition”. In CVPR, pages 6026–6035, 2018
[6] E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool. “Soft-to-hard vector quantization for end-to-end learning compressible representations.” In NIPS, pages 1141–1151, 2017.
[7] E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. Van Gool. “Generative adversarial networks for extreme learned image compression.” arXiv preprint arXiv:1804.02958, 2018.
[8] J. Ball´e, V. Laparra, and E. P. Simoncelli. “End-to-end optimized image compression.” arXiv preprint arXiv:1611.01704, 2016.
[9] J. Ball´e, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston. “Variational image compression with a scale hyperprior.” arXiv preprint arXiv:1802.01436, 2018.
[10] L. Theis, W. Shi, A. Cunningham, and F. Husz´ar. “Lossy image compression with compressive autoencoders.” arXiv preprint arXiv:1703.00395, 2017.
[11] G. Toderici, S. M. O’Malley, S. J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, and R. Sukthankar. “Variable rate image compression with recurrent neural networks”. arXiv preprint arXiv:1511.06085, 2015.
[12] G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell. “Full resolution image compression with recurrent neural networks.” In CVPR, pages 5435 – 5443, 2017.
[13] N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. Jin Hwang, J. Shor, and G. Toderici. “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks.” In CVPR, June 2018.
[14] M. Li,W. Zuo, S. Gu, D. Zhao, and D. Zhang. “Learning Convolutional networks for content-weighted image compression.” In CVPR, June 2018.
[15] O. Rippel and L. Bourdev. “Real-time adaptive image compression.” In ICML, 2017.
[16] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool. “Conditional probability models for deep image compression.” In CVPR, number 2, page 3, 2018.
[17] M. H. Baig, V. Koltun, and L. Torresani. “Learning to inpaint for image compression.” In NIPS, pages 1246–1255, 2017.
[18] T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra. “Overview of the h. 264/avc video coding standard.” TCSVT, 13(7):560–576, 2003.
[19] G. J. Sullivan, J.-R. Ohm, W.-J. Han, T. Wiegand, et al. “Overview of the high efficiency video coding(hevc) standard.” TCSVT, 22(12):1649–1668, 2012.
[20] T. Chen, H. Liu, Q. Shen, T. Yue, X. Cao, and Z. Ma. “Deepcoder: A deep neural network based video compression.” In VCIP, pages 1–4. IEEE, 2017.
[21] Z. Liu, X. Yu, Y. Gao, S. Chen, X. Ji, and D. Wang. “Cu partition mode decision for hevc hardwired intra encoder using convolution neural network.” TIP, 25(11):5088–5103, 2016.
[22] G. Lu,W. Ouyang, D. Xu, X. Zhang, Z. Gao, and M.-T. Sun. “Deep kalman filtering network for video compression artifact reduction.” In ECCV, September 2018.
[23] R. Song, D. Liu, H. Li, and F. Wu. “Neural network-based arithmetic coding of intra prediction modes in hevc.” In VCIP, pages 1–4. IEEE, 2017.
[24] Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Kr¨ahenb¨uhl. “Compressed Video Action Recognition.” arXiv:1712.00636v2 [cs.CV] 29 Mar 2018.
[25] Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. “Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5).” arXiv:1311.2524v5 [cs.CV] 22 Oct 2014.
[26] Ross Girshick. “Fast R-CNN.” arXiv:1504.08083v2 [cs.CV] 27 Sep 2015.
[27] Joseph Redmon, Ali Farhadi. “YOLO9000: Better, Faster, Stronger.” arXiv:1612.08242v1 [cs.CV] 25 Dec 2016.
[28] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Application.” arXiv:1704.04861v1 [cs.CV] 17 Apr 2017.
[29] Shiyao Wang, Hongchao Lu, Zhidong Deng. “Fast Object Detection in Compressed Video.” arXiv:1811.11057v2 [cs.CV] 31 Jan 2019.
[30] https://www.microsoft.com/en-us/microsoft- 365/blog/2020/04/09/remote-work-trend-report-meetings/

Keywords
COVID-19, video, bandwidth, deep learning, compression.