No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior

Sewoong Ahn; Sanghoon Lee

doi:10.23919/APSIPA.2018.8659706

No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior

Sewoong Ahn, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.

Original language	English
Title of host publication	2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1513-1517
Number of pages	5
ISBN (Electronic)	9789881476852
DOIs	https://doi.org/10.23919/APSIPA.2018.8659706
Publication status	Published - 2019 Mar 4
Event	10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States Duration: 2018 Nov 12 → 2018 Nov 15

Publication series

Name	2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference	10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/Territory	United States
City	Honolulu
Period	18/11/12 → 18/11/15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Koreagovernment (MSIT) (No. 2016R1A2B2014525).

Publisher Copyright:
© 2018 APSIPA organization.

All Science Journal Classification (ASJC) codes

Information Systems

Access to Document

10.23919/APSIPA.2018.8659706

Cite this

Ahn, S., & Lee, S. (2019). No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (pp. 1513-1517). [8659706] (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/APSIPA.2018.8659706

Ahn, Sewoong ; Lee, Sanghoon. / No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior. 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1513-1517 (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings).

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abstract = "The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.",

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Ahn, S & Lee, S 2019, No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior. in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings., 8659706, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1513-1517, 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018, Honolulu, United States, 18/11/12. https://doi.org/10.23919/APSIPA.2018.8659706

No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior. / Ahn, Sewoong; Lee, Sanghoon.

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1513-1517 8659706 (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Ahn S, Lee S. No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1513-1517. 8659706. (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings). doi: 10.23919/APSIPA.2018.8659706

No-Reference Video Quality Assessment based on Convolutional Neural Network and Human Temporal Behavior

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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