WebRTC based video quality assessment
The video quality assessment (VQA) methods have been the default mechanism to evaluate the quality of a video. Notably for online video consumption, the video quality might be inconsistent due to many variables that could affect the quality of experience (QOE) for the end user. Therefore, a VQA m...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156466 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The video quality assessment (VQA) methods have been the default mechanism to evaluate
the quality of a video. Notably for online video consumption, the video quality might be
inconsistent due to many variables that could affect the quality of experience (QOE) for the
end user. Therefore, a VQA metric is important to provide a better QOE for the end user. One
of the metrics that can be used to determine the video quality is temporal masking. Temporal
masking has been researched on since the early days and is a technique to delude
individuals into thinking they can identify any differences with a complex spatial and
temporal background. Research have been done to evaluate the spatio-temporal in a video
using 3D CNN and the results are better compared to other state-of-the-arts technology. In
this paper, we aim to explore how Transformers model will perform compared to traditional
method of using Convolutional Neural Network (CNN). Transformers have recently started
to show effectiveness in solving computer vision tasks. The Transformers' capability to
model long-range relationships makes it an obvious choice for learning temporal information
over numerous frames for video comprehension problems. |
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