A Fast and Efficient No-Reference Video Quality Assessment Algorithm Using Video Action Recognition Features

N, Suresh and Mylavarapu, Pavan Manesh and Mahankali, Naga Sailaja and Channappayya, Sumohana S. (2022) A Fast and Efficient No-Reference Video Quality Assessment Algorithm Using Video Action Recognition Features. In: 27th National Conference on Communications, NCC 2022, 24 May 2022 through 27 May 2022, Virtual, Online.

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This work addresses the problem of efficient noreference video quality assessment (NR-VQA). The motivation for this work is that even the best and fastest VQA algorithms do not achieve real-time performance. The speed of quality evaluation is impeded primarily by the spatio-temporal feature extraction stage. This impediment is common to both traditional as well as deep learning models. To address this issue, we explore the efficacy of features used in the action recognition problem for NR- VQA. Specifically, we leverage the efficiency offered by Gate Shift Module (GSM) in extracting spatio-temporal features. A simple yet effective improvement to the GSM model is proposed by adding the self-attention module. We first show that GSM features are indeed effective for NR-VQA. We then demonstrate a speed-up that is orders of magnitude faster than the current state-of-the-art VQA algorithms, albeit at the cost of overall performance. We evaluate the efficacy of our algorithm on both Standard Dynamic Range (SDR) and High Dynamic Range (HDR) datasets like KoNViD-1K, LIVE VQC, HDR. © 2022 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, Sumohana S.https://orcid.org/0000-0002-5880-4023
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: GSM; high dynamic range; self-attention; SVR; video quality assessment
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 02 Aug 2022 11:02
Last Modified: 02 Aug 2022 11:02
URI: http://raiith.iith.ac.in/id/eprint/10064
Publisher URL: http://doi.org/10.1109/NCC55593.2022.9806466
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