Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features

Jari Korhonen; Yicheng Su; Junyong You; Steven Hicks; Cise Midoglu

doi:10.1145/3474085.3477933

Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features

Jari Korhonen, Yicheng Su, Junyong You, Steven Hicks, Cise Midoglu

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

1 Citation (Scopus)

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.

Original language	English
Title of host publication	MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
Publisher	Association for Computing Machinery, Inc
Pages	3622-3626
Number of pages	5
ISBN (Electronic)	9781450386517
DOIs	https://doi.org/10.1145/3474085.3477933
Publication status	Published - 17 Oct 2021
Event	29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China Duration: 20 Oct 2021 → 24 Oct 2021

Publication series

Name	MM: International Multimedia Conference

Conference

Conference	29th ACM International Conference on Multimedia, MM 2021
Country/Territory	China
City	Virtual, Online
Period	20/10/21 → 24/10/21

Bibliographical note

Funding Information:
This work was supported in part by Natural Science Foundation of China under grant 61772348, and in part by Guangdong "Pearl River Talent Recruitment Program" under Grant 2019ZT08X603.

Keywords

convolutional neural network
human visual system
machine learning
video quality assessment

Access to Document

10.1145/3474085.3477933Licence: Unspecified

Cite this

Korhonen, J., Su, Y., You, J., Hicks, S., & Midoglu, C. (2021). Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features. In MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (pp. 3622-3626). (MM: International Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3474085.3477933

Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features. / Korhonen, Jari; Su, Yicheng; You, Junyong et al.
MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia. Association for Computing Machinery, Inc, 2021. p. 3622-3626 (MM: International Multimedia Conference).

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

Korhonen, J, Su, Y, You, J, Hicks, S & Midoglu, C 2021, Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features. in MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia. MM: International Multimedia Conference, Association for Computing Machinery, Inc, pp. 3622-3626, 29th ACM International Conference on Multimedia, MM 2021, Virtual, Online, China, 20/10/21. https://doi.org/10.1145/3474085.3477933

Korhonen J, Su Y, You J, Hicks S, Midoglu C. Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features. In MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia. Association for Computing Machinery, Inc. 2021. p. 3622-3626. (MM: International Multimedia Conference). Epub 2021 Oct 17. doi: 10.1145/3474085.3477933

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title = "Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features",

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.",

keywords = "convolutional neural network, human visual system, machine learning, video quality assessment",

author = "Jari Korhonen and Yicheng Su and Junyong You and Steven Hicks and Cise Midoglu",

note = "Funding Information: This work was supported in part by Natural Science Foundation of China under grant 61772348, and in part by Guangdong {"}Pearl River Talent Recruitment Program{"} under Grant 2019ZT08X603. ; 29th ACM International Conference on Multimedia, MM 2021 ; Conference date: 20-10-2021 Through 24-10-2021",

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doi = "10.1145/3474085.3477933",

language = "English",

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AU - Korhonen, Jari

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AU - Hicks, Steven

AU - Midoglu, Cise

N1 - Funding Information: This work was supported in part by Natural Science Foundation of China under grant 61772348, and in part by Guangdong "Pearl River Talent Recruitment Program" under Grant 2019ZT08X603.

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N2 - 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.

AB - 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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BT - MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia

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Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features

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