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View all- Kossi KCoulombe SDesrosiers CGagnon G(2022)No-Reference Video Quality Assessment Using Distortion Learning and Temporal AttentionIEEE Access10.1109/ACCESS.2022.316744610(41010-41022)Online publication date: 2022
Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features
Pages 3622 - 3626
Abstract
Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.
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Index Terms
- Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features
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Published In
October 2021
5796 pages
ISBN:9781450386517
DOI:10.1145/3474085
- General Chairs:
- Heng Tao Shen,
- Yueting Zhuang,
- John R. Smith,
- Program Chairs:
- Yang Yang,
- Pablo Cesar,
- Florian Metze,
- Balakrishnan Prabhakaran
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View all- Kossi KCoulombe SDesrosiers CGagnon G(2022)No-Reference Video Quality Assessment Using Distortion Learning and Temporal AttentionIEEE Access10.1109/ACCESS.2022.316744610(41010-41022)Online publication date: 2022
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