3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms

Xue Li; Jiali Qiu

doi:10.62762/TIOT.2024.369369

Article Information

Published in ICCK Transactions on Internet of Things

Volume/Issue Volume 2, Issue 1, 2024

Pages 8-19

Abstract

In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model employs a two-stream 3D convolutional neural network to integrate spatial and temporal streams for capturing video distortion details, with a coding layer configured to remove redundant distortion information. We validated our approach using the LIVE and CSIQ datasets, comparing its performance against the V-BLIINDS and VIDEO schemes across different packet loss rates. Furthermore, we simulated the client-server interaction using a subset of the dataset and assessed the scheme's time efficiency. Our results indicate that the proposed scheme offers a highly efficient solution for video quality assessment.

Graphical Abstract

3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms

Keywords

Video quality assessment 3D CNN Packet loss rate SRCC PLCC

Funding

This work was supported without any funding.

References

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Cite This Article

APA Style

Li, X., & Qiu, J. (2024). 3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms. ICCK Transactions on Internet of Things, 2(1), 8–19 https://doi.org/10.62762/TIOT.2024.369369

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TY  - JOUR
AU  - Li, Xue
AU  - Qiu, Jiali
PY  - 2024
DA  - 2024/01/14
TI  - 3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms
JO  - ICCK Transactions on Internet of Things
T2  - ICCK Transactions on Internet of Things
JF  - ICCK Transactions on Internet of Things
VL  - 2
IS  - 1
SP  - 8
EP  - 19
DO  - 10.62762/TIOT.2024.369369
UR  - https://www.icck.org/article/abs/TIOT.2024.369369
KW  - Video quality assessment
KW  - 3D CNN
KW  - Packet loss rate
KW  - SRCC
KW  - PLCC
AB  - In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model employs a two-stream 3D convolutional neural network to integrate spatial and temporal streams for capturing video distortion details, with a coding layer configured to remove redundant distortion information. We validated our approach using the LIVE and CSIQ datasets, comparing its performance against the V-BLIINDS and VIDEO schemes across different packet loss rates. Furthermore, we simulated the client-server interaction using a subset of the dataset and assessed the scheme's time efficiency. Our results indicate that the proposed scheme offers a highly efficient solution for video quality assessment.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Li20243D,
  author = {Xue Li and Jiali Qiu},
  title = {3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms},
  journal = {ICCK Transactions on Internet of Things},
  year = {2024},
  volume = {2},
  number = {1},
  pages = {8-19},
  doi = {10.62762/TIOT.2024.369369},
  url = {https://www.icck.org/article/abs/TIOT.2024.369369},
  abstract = {In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model employs a two-stream 3D convolutional neural network to integrate spatial and temporal streams for capturing video distortion details, with a coding layer configured to remove redundant distortion information. We validated our approach using the LIVE and CSIQ datasets, comparing its performance against the V-BLIINDS and VIDEO schemes across different packet loss rates. Furthermore, we simulated the client-server interaction using a subset of the dataset and assessed the scheme's time efficiency. Our results indicate that the proposed scheme offers a highly efficient solution for video quality assessment.},
  keywords = {Video quality assessment, 3D CNN, Packet loss rate, SRCC, PLCC},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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