Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor.advisor | Chan, Yui Lam (EEE) | en_US |
| dc.creator | Zhou, Junlong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13870 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Video quality assessment using deep learning : a spatiotemporal approach | en_US |
| dcterms.abstract | This dissertation presents a novel approach to video quality assessment (VQA) based on two deep learning techniques: Transformers and 3D-convolutional neural networks (3D-CNNs). The primary focus is on using spatiotemporal learning for effective VQA. | en_US |
| dcterms.abstract | The first part of the study looks at the challenges associated with VQA and emphasizes the need for models that can accurately capture both temporal and spatial information in video sequences. To address these problems, we propose a unique framework that blends 3D-CNNs with Transformers. The 3D-CNNs are used to extract spatiotemporal features from the multi-frame by grouping the films into three-frame segments to extract short-term features. The long-term features included inside these short-term features are subsequently extracted using the Transformer model. | en_US |
| dcterms.abstract | Our proposed model exceeds the state-of-the-art methods already in use and performs well in the VQA challenge. The results show the potential of deep learning approaches in this field as well as the importance of integrating spatiotemporal learning into VQA. | en_US |
| dcterms.extent | 1 volume (unpaged) : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2023 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8273.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.67 MB | Adobe PDF | View/Open |
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