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Author: Domonkos Varga

Affiliation: Department of Networked Systems and Services, Budapest University of Technology, Hungary

Keyword(s): No-reference Video Quality Assessment, Convolutional Neural Network.

Abstract: Video quality assessment (VQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to surveillance systems. Furthermore, the measurement of video quality requires an extensive investigation of image and video features. In this paper, we introduce a novel feature extraction method for no-reference video quality assessment (NR-VQA) relying on visual features extracted from multiple Inception modules of pretrained convolutional neural networks (CNN). Hence, we show a solution which incorporates both intermediate- and high-level deep representations from a CNN to predict digital videos’ perceptual quality. Second, we demonstrate that processing all frames of a video to be evaluated is unnecessary and examining only the so-called intra-frames saves computational time and improves performance significantly. The proposed architecture was trained and tested on the recently published KoNViD-1k database.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Varga, D. (2020). Multi-pooled Inception Features for No-reference Video Quality Assessment. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 338-347. DOI: 10.5220/0008978503380347

@conference{visapp20,
author={Domonkos Varga.},
title={Multi-pooled Inception Features for No-reference Video Quality Assessment},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={338-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008978503380347},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Multi-pooled Inception Features for No-reference Video Quality Assessment
SN - 978-989-758-402-2
IS - 2184-4321
AU - Varga, D.
PY - 2020
SP - 338
EP - 347
DO - 10.5220/0008978503380347
PB - SciTePress

- Science and Technology Publications, Lda.
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