Video-Quality-Assessment-Flask-Dashboard

About

This is a web-based interface for an algorithm called DOVER (Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content) that would allow users to interact with DOVER through a web browser, making it more accessible and convenient for users who want to assess the quality of their videos. This interface is built using a web development framework called Flask, which is a lightweight Python framework that allows developers to build web applications quickly.

DOVER is designed to disentangle aesthetic and technical effects when evaluating the quality of a video. Aesthetic effects refer to the visual appeal of the video, such as the composition, lighting, and colour grading, while technical effects refer to the technical aspects of the video, such as resolution, frame rate, and bitrate. By separating these two types of effects, DOVER aims to provide a more accurate and comprehensive assessment of the overall quality of the video.

Getting Started

Use the terminal or an Anaconda Prompt for the following steps:

Clone this repo:

git clone https://github.com/KrishnaPhalgun28/Video-Quality-Assessment-Flask-Dashboard.git

Change to the repo directory:

cd Video-Quality-Assessment-Flask-Dashboard

Create the environment from the `environment.yml` file:

conda env create -f environment.yml

Activate the new environment:

conda activate vqa

Verify that the new environment was installed correctly:

conda info --envs

Run the app:

python ./dashboard.py

Logs

Index	Filename	Size	Duration	Height	Width	Technical	Aesthetic
0	10053703032	1.91 MB	8 s	720 px	406 px	-0.5506605973639963	-1.1236979548934467
1	10053703033	2.2 MB	7 s	720 px	1280 px	0.3083066024870503	0.7302504056404503
2	10053703034	1.33 MB	5 s	540 px	960 px	-0.5312615059468662	-0.4483086324642102
3	10053703036	3.19 MB	5 s	1080 px	1920 px	-1.9432594891608730	-1.2976735513429503

Link to demo

https://drive.google.com/file/d/1wNPHJgiWwC_Uq_YTvv-ohMfjar3j5zdf/view?usp=share_link

References

Demo Video	Link
10053703032	https://github.com/QualityAssessment/DOVER/tree/master/demo
10053703033	https://github.com/QualityAssessment/DOVER/tree/master/demo
10053703034	https://github.com/QualityAssessment/FAST-VQA-and-FasterVQA/tree/dev/demos
10053703036	https://www.pexels.com/video/a-couple-in-a-park-overseeing-sydney-city-skyline-and-the-opera-house-3209971/

@article{wu2022disentanglevqa,
  title={Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content},
  author={Wu, Haoning and Liao, Liang and Chen, Chaofeng and Hou, Jingwen and Wang, Annan and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={arXiv preprint arXiv:2211.04894},
  year={2022}
}

@article{wu2022fastquality,
  title={FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling},
  author={Wu, Haoning and Chen, Chaofeng and Hou, Jingwen and Liao, Liang and Wang, Annan and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={Proceedings of European Conference of Computer Vision (ECCV)},
  year={2022}
}

@misc{end2endvideoqualitytool,
  title = {Open Source Deep End-to-End Video Quality Assessment Toolbox},
  author = {Wu, Haoning},
  year = {2022},
  url = {http://github.com/timothyhtimothy/fast-vqa}
}

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