A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud

Authors

  • Xue Li Xidian University, Xi’an, China
  • Jiali Qiu Xi’an Jiaotong University,Xi’an, China

DOI

https://doi.org/10.52810/TIOT.2021.100063

Keywords:

Video quality assessment, 3D CNN, packet loss rate, SRCC, PLCC

Abstract

As the rapid development of big data and the artificial intelligence technology, users prefer uploading more and more local files to the cloud server to reduce the pressure of local storage, but when users upload more and more duplicate files , not only wasting the network bandwidth, but also bringing much more inconvenience to the server management, especially images and videos. To solve the problems above, we design a multi-parameter video quality assessment model based on 3D convolutional neural network in the video deduplication system, we use a method similar to analytic hierarchy process to comprehensively evaluate the impact of packet loss rate, codec, frame rate, bit rate, resolution on video quality, and build a two-stream 3D convolutional neural network from the spatial flow and timing flow to capture the details of video distortion, set the coding layer to remove redundant distortion information. Finally, the LIVE and CSIQ data sets are used for experimental verification, we compare the performance of the proposed scheme with the V-BLIINDS scheme and VIDEO scheme under different packet loss rates. We also use the part of data set to simulate the interaction process between the client and the server, then test the time cost of the scheme. On the whole, the scheme proposed in this paper has a high quality assessment efficiency.

Author Biography

Xue Li, Xidian University, Xi’an, China

References

Yang X , Lu R , Choo K K R , et al. Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud[J]. IEEE Transactions on Big Data, 2017:1-1.

Wu X, Hauptmann A G, Ngo C W. Practical elimination of near-duplicates from web video search[C]. Proceedings of the 15th ACM international conference on Multimedia. ACM, 2007: 218-227.

J. Y. Yao and G. Liu, "Bitrate-based no-reference video quality assessment combining the visual perception of video contents", IEEE Trans. Broadcast., vol. 65, no. 3, pp. 546-557, Sep. 2019.

D. Valderrama and N. Gómez, Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment, Advances in Multimedia, Vol. 2016, 2016.

J. Søgaard, S. Forchhammer and J. Korhonen, Video quality assessment and machine learning: Performance and interpretability, in: 7th International Workshop on Quality of Multimedia Experience (QoMEX), 2015.

Seshadrinathan K, Soundararajan R, Bovik A C, et al. A subjective study to evalute video quality assessment algorithms[C]. //IS&T/SPIE Electronic Imaging. San Jose: IEEE, 2010: 75270H.

Saad M A, Bovik A C, Charrier C. Blind prediction of natural video quality [J]. IEEE Transactions on image Processing, 2014, 23 (3): 1352-1365.

Mittal A, Soundararajan R, Bovik A C. Making a completely blind image quality analyzer [J]. IEEE Signal processing Letters, 2013, 22 (3): 209-212.

W. Loh and D.B.L. Bong, A Just Noticeable Difference-Based Video Quality Assessment Method with Low Computational Complexity, Sensing and Imaging, Vol. 19, Article number: 33, 2018. doi: 10.1007/s11220-018-0216-9.

Z. Cheng, L. Ding, W. Huang, F. Yang and L. Qian, A unified QoE prediction framework for HEVC encoded video streaming over wireless networks, in: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Cagliari, 2017, pp. 1–6.

L. Anegekuh, L. Sun, E. Jammeh, I. Mkwawa and E. Ifeachor, Content-based video quality prediction for HEVC encoded videos streamed over packet networks, In IEEE Transactions on Multimedia 17(8) (2015), 1323–1334.

M. Alreshoodi, A.O. Adeyemi-Ejeye, J. Woods and S.D. Walker, Fuzzy logic inference system-based hybrid quality prediction model for wireless 4k UHD H.265-coded video streaming, In IET Networks 4(6) (2015), 296–303.

Y. Zhang, X. Gao, L. He, W. Lu and R. He, "Blind video quality assessment with weakly supervised learning and resampling strategy", IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 8, pp. 2244-2255, Aug. 2019

J.F.B. Valderrama and D.J.L. Valderrama, On LAMDA clustering method based on typicality degree and intuitionistic fuzzy sets, Expert Systems with Applications. 107 (2018), 196–221. doi:10.1016/j.eswa.2018.04.022.

Y. Li et al., "No-reference video quality assessment with 3D shearlet transform and convolutional neural networks", IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 6, pp. 1044-1057, Jun. 2016.

M. Agarla, L. Celona and R. Schettini, "No-reference quality assessment of in-capture distorted videos", J. Imag., vol. 6, no. 8, 2020.

J. Nightingale, P. Salva-Garcia, J.M.A. Calero and Q. Wang, 5G-QoE: QoE modelling for Ultra-HD video streaming in 5G networks, IEEE Transactions on Broadcasting 64(2) (2018), 621–634. doi: 10.1109/TBC.2018.2816786.

M. Narwaria and W. Lin, Machine Learning Based Modeling of Spatial and Temporal Factors for Video Quality Assessment, in: Proc. 18th IEEE International Conference on Image Processing (ICIP), 2011, pp. 2513–2516.

P. Debajyoti and V. Vajirasak, A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device, Advances in Multimedia, Vol. 2017, Article ID 8317590, 2017. doi: 10.1155/2017/8317590.

L. Qian et al., "No-Reference Nonuniform Distorted Video Quality Assessment Based on Deep Multiple Instance Learning," in IEEE MultiMedia, vol. 28, no. 1, pp. 28-37, 1 Jan.-March 2021, doi: 10.1109/MMUL.2020.3034338.

Chen, P.; Li, L.; Ma, L.; Wu, J.; Shi, G. RIRNet: Recurrent-In-Recurrent Network for Video Quality Assessment. In Proceedings of the ACM International Conference on Multimedia, Dublin, Ireland, 26–29 October 2020; pp. 834–842. 28.

Li, D.; Jiang, T.; Jiang, M. Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training. Int. J. Comput. Vis. 2021. [CrossRef].

L. Qian et al., "No-Reference Nonuniform Distorted Video Quality Assessment Based on Deep Multiple Instance Learning," in IEEE MultiMedia, vol. 28, no. 1, pp. 28-37, 1 Jan.-March 2021, doi: 10.1109/MMUL.2020.3034338.

Peining Zhen,Hai-Bao Chen,Yuan Cheng, et al., Fast Video Facial Expression Recognition by a Deeply Tensor-Compressed LSTM Neural Network for Mobile Devices. Association for Computing Machinery. November 2021, Article No.: 23, pp 1–26. https://doi.org/10.1145/3464941.

A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud

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Published

2021-09-04

How to Cite

Li, X., & Qiu, J. (2021). A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud. ASP Transactions on Internet of Things, 1(2), 14–22. https://doi.org/10.52810/TIOT.2021.100063

Issue

Section

Regular Paper