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The analysis of network video quality assessment based on different fuzzy neural network

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Abstract

Nowadays, people watch network video through any way, such as mobile phone, tables. However, during the transmission process of network video, the video quality may be impaired by many factors. So how to assess the video quality has become a hot research topic in the academic community. This paper proposes objective assessment method based on fuzzy neural network. At first, the impairment factors of video quality are introduced, next the experimental environment is built, and two fuzzy neural network models are used to build objective assessment method. By adjusting the model structure and training times, the objective scores of video quality are calculated. At the same time, other recent objective methods are compared with the proposed method. Lastly the advantages of two models are analyzed, and the detail process of them will be discussed.

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References

  1. Shang Z, Ebenezer JP, Yongjun Wu et al (2022) Study of the subjective and objective quality of high motion live streaming videos[J]. IEEE Trans Image Process 31:1027–1041

    Article  ADS  PubMed  Google Scholar 

  2. Li T, Min X, Zhao H et al (2021) Subjective and objective quality assessment of compressed screen content videos[J]. IEEE Trans Broadcast 67(2):438–449

    Article  ADS  Google Scholar 

  3. Shi Z, Huang C (2022) The research of OTT video quality assessment method[J]. SIViP 16(02):569–577

    Article  Google Scholar 

  4. Kancharla P, Channappayya SS (2022) Completely blind quality assessment of user generated video content[J]. IEEE Trans Image Process 31:263–274

    Article  ADS  PubMed  Google Scholar 

  5. Jin Y, Chen M, Goodall T et al (2021) Subjective and objective quality assessment of 2D and 3D foveated video compression in virtual reality[J]. IEEE Trans Image Process 30:5905–5919

    Article  ADS  PubMed  Google Scholar 

  6. Jin C, Peng Z, Chen F et al (2022) Subjective and objective video quality assessment for Windowed-6DoF synthesized videos[J]. IEEE Trans Broadcast 68(3):594–608

    Article  Google Scholar 

  7. Guan X, Li F, Huang Z et al (2022) Study of subjective and objective quality assessment of night-time videos[J]. IEEE Trans Circuits Syst Video Technol 32(10):6627–6641

    Article  Google Scholar 

  8. Sudeng Hu, Jin L, Wang H et al (2017) Objective video quality assessment based on perceptually weighted mean squared error[J]. IEEE Trans Circuits Syst Video Technol 27(9):1844–1855

    Article  Google Scholar 

  9. Shang X, Liang J, Wang G et al (2019) Color-sensitivity-based combined PSNR for objective video quality assessment[J]. IEEE Trans Circuits Syst Video Technol 29(5):1239–1250

    Article  Google Scholar 

  10. Bampis CG, Li Z, Bovik AC (2019) Spatiotemporal feature integration and model fusion for full reference video quality assessment[J]. IEEE Trans Circuits Syst Video Technol 29(8):2256–2270

    Article  Google Scholar 

  11. Zheng Qi, Zhengzhong Tu, Zeng X et al (2022) A completely blind video quality evaluator[J]. IEEE Signal Process Lett 29:2228–2232

    Article  ADS  Google Scholar 

  12. Chen Z, Liao N, Xiaodong Gu et al (2016) Hybrid distortion ranking tuned bitstream-layer video quality assessment[J]. IEEE Trans Circuits Syst Video Technol 26(6):1029–1043

    Article  Google Scholar 

  13. Ghadiyaram D, Pan J, Bovik AC (2019) A subjective and objective study of stalling events in mobile streaming videos[J]. IEEE Trans Circuits Syst Video Technol 29(1):183–197

    Article  Google Scholar 

  14. Appina B, Channappayya SS (2018) Full-reference 3-D video quality assessment using scene component statistical dependencies[J]. IEEE Signal Process Lett 25(6):823–827

    Article  ADS  Google Scholar 

  15. Ghadiyaram D, Pan J, Bovik AC (2018) Learning a continuous-time streaming video QoE model[J]. IEEE Trans Image Process 27(5):2257–2271

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  16. Zhang F, Bull DR (2016) A perception-based hybrid model for video quality assessment[J]. IEEE Trans Circuits Syst Video Technol 26(6):1017–1028

    Article  Google Scholar 

  17. Galkandage C, Calic J, Dogan S et al (2021) Full-reference stereoscopic video quality assessment using a motion sensitive HVS model[J]. IEEE Trans Circuits Syst Video Technol 31(2):452–466

    Article  Google Scholar 

  18. Zhu K, Li C, Asari V et al (2015) No-reference video quality assessment based on artifact measurement and statistical analysis[J]. IEEE Trans Circuits Syst Video Technol 25(4):533–545

    Article  Google Scholar 

  19. Tao X, Duan Y, Mai Xu et al (2019) Learning QoE of mobile video transmission with deep neural network: a data-driven approach[J]. IEEE J Select Areas Commun 37(6):1337–1348

    Article  Google Scholar 

  20. Zhang Yu, Gao X, He L et al (2019) Blind video quality assessment with weakly supervised learning and resampling strategy[J]. IEEE Trans Circuits Syst Video Technol 29(8):2244–2255

    Article  Google Scholar 

  21. Mok RKP, Chan EWW, Chang RKC (2011) Measuring the Quality of Experience of HTTP Video Streaming. 2011 12th IFIP/IEEE International Symposium on Integrated Network Management and Workshops, Dublin, Ireland, May 23–27, pp. 485–492

  22. Kawano T, Yamagishi K, Watanabe K et al (2010) No Reference Video Quality Assessment Model for Video Streaming Services. 2010 18th International Packet Video Workshop, Dec 13–14, 2010, Hong Kong, China, pp. 158–164

  23. Zheng X, Yang B, Liu Y et al (2009) Blockiness Evaluation for Reducing Blocking Artifacts in Compressed Images. 2009 Digest of Technical Papers International Conference Electronics, Jan 10-14, 2009, Las Vegas, NV, USA, pp. 1-2

  24. Guo H, Zeng W, Shi Y et al (2020) Kernel granger causality based on back propagation neural network fuzzy inference system on fMRI data. IEEE Trans Neural Syst Rehabil Eng 28(5):1049–1058

    Article  PubMed  Google Scholar 

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Acknowledgements

This work is supported by Science and Technology Research Project of Jiangxi Provincial Department of Education (GJJ2200529).

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  1. School of Software & Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Zhiming Shi

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  1. Zhiming Shi
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Correspondence to Zhiming Shi.

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Shi, Z. The analysis of network video quality assessment based on different fuzzy neural network. Multimed Tools Appl 83, 32177–32189 (2024). https://doi.org/10.1007/s11042-023-16834-4

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