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http://hdl.handle.net/10397/90505
| Title: | No-reference video quality assessment metric using spatiotemporal features through LSTM | Authors: | Kwong, NW Tsang, SH Chan, YL Lun, DPK Lee, TK |
Issue Date: | 2021 | Source: | Proceedings of SPIE : the International Society for Optical Engineering, 2021, v. 11766, 1176629 | Abstract: | Nowadays, a precise video quality assessment (VQA) model is essential to maintain the quality of service (QoS). However, most existing VQA metrics are designed for specific purposes and ignore the spatiotemporal features of nature video. This paper proposes a novel general-purpose no-reference (NR) VQA metric adopting Long Short-Term Memory (LSTM) modules with the masking layer and pre-padding strategy, namely VQA-LSTM, to solve the above issues. First, we divide the distorted video into frames and extract some significant but also universal spatial and temporal features that could effectively reflect the quality of frames. Second, the data preprocessing stage and pre-padding strategy are used to process data to ease the training for our VQA-LSTM. Finally, a three-layer LSTM model incorporated with masking layer is designed to learn the sequence of spatial features as spatiotemporal features and learn the sequence of temporal features as the gradient of temporal features to evaluate the quality of videos. Two widely used VQA database, MCL-V and LIVE, are tested to prove the robustness of our VQA-LSTM, and the experimental results show that our VQA-LSTM has a better correlation with human perception than some state-of-the-art approaches. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. | Publisher: | SPIE-International Society for Optical Engineering | Journal: | Proceedings of SPIE : the International Society for Optical Engineering | ISSN: | 0277-786X | EISSN: | 1996-756X | DOI: | 10.1117/12.2590406 | Description: | International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only | Rights: | Copyright 2021 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. The following publication Ngai-Wing Kwong, Sik-Ho Tsang, Yui-Lam Chan, Daniel Pak-Kong Lun, and Tsz-Kwan Lee "No-reference video quality assessment metric using spatiotemporal features through LSTM", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176629 (13 March 2021) is available at https://dx.doi.org/10.1117/12.2590406 |
| Appears in Collections: | Conference Paper |
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| 2235_Kwong_No_Reference_Video.pdf | Pre-Published version | 977.28 kB | Adobe PDF | View/Open |
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