An evaluation of video quality assessment metrics for passive gaming video streaming

Nabajeet Barman; Steven Schmidt; Saman Zadtootaghaj; Maria G. Martini; Sebastian M├Âller

An evaluation of video quality assessment metrics for passive gaming video streaming

Nabajeet Barman
, Steven Schmidt
, Saman Zadtootaghaj
, Maria G. Martini
, Sebastian M├Âller

Research output: Contribution to conference › Paper › peer-review

Abstract

Video quality assessment is imperative to estimate the user experience to provide a reasonable Quality of Experience in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper an objective and subjective quality assessment study of gaming videos considering passive streaming applications. For subjective quality assessment, we consider six different gaming video sequences and 15 resolution-bitrate pairs. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a very promising alternative as a no-reference metric.

Original language	English
Publication status	E-pub ahead of print - 12 Jun 2018
Event	23rd Packet Video Workshop 2018 - Amsterdam, Netherlands Duration: 12 Jun 2018 → 15 Jun 2018

Conference

Conference	23rd Packet Video Workshop 2018
Period	12/06/18 → 15/06/18

Bibliographical note

Note: This paper was published in PV '18 Proceedings of the 23rd Packet Video Workshop (2018), pages 7-12. ISBN: 9781450357739

Organising Body: Association for Computing Machinery Special Interest Group on Multimedia

Keywords

Computer science and informatics

Access to Document

http://dx.doi.org/10.1145/3210424.3210434

Cite this

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title = "An evaluation of video quality assessment metrics for passive gaming video streaming",

abstract = "Video quality assessment is imperative to estimate the user experience to provide a reasonable Quality of Experience in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper an objective and subjective quality assessment study of gaming videos considering passive streaming applications. For subjective quality assessment, we consider six different gaming video sequences and 15 resolution-bitrate pairs. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a very promising alternative as a no-reference metric.",

keywords = "Computer science and informatics",

author = "Nabajeet Barman and Steven Schmidt and Saman Zadtootaghaj and Martini, \{Maria G.\} and Sebastian M├{\^A}ller",

note = "Note: This paper was published in PV '18 Proceedings of the 23rd Packet Video Workshop (2018), pages 7-12. ISBN: 9781450357739 Organising Body: Association for Computing Machinery Special Interest Group on Multimedia; 23rd Packet Video Workshop 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

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TY - CONF

T1 - An evaluation of video quality assessment metrics for passive gaming video streaming

AU - Barman, Nabajeet

AU - Schmidt, Steven

AU - Zadtootaghaj, Saman

AU - Martini, Maria G.

AU - M├Âller, Sebastian

N1 - Note: This paper was published in PV '18 Proceedings of the 23rd Packet Video Workshop (2018), pages 7-12. ISBN: 9781450357739 Organising Body: Association for Computing Machinery Special Interest Group on Multimedia

PY - 2018/6/12

Y1 - 2018/6/12

N2 - Video quality assessment is imperative to estimate the user experience to provide a reasonable Quality of Experience in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper an objective and subjective quality assessment study of gaming videos considering passive streaming applications. For subjective quality assessment, we consider six different gaming video sequences and 15 resolution-bitrate pairs. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a very promising alternative as a no-reference metric.

AB - Video quality assessment is imperative to estimate the user experience to provide a reasonable Quality of Experience in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper an objective and subjective quality assessment study of gaming videos considering passive streaming applications. For subjective quality assessment, we consider six different gaming video sequences and 15 resolution-bitrate pairs. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a very promising alternative as a no-reference metric.

KW - Computer science and informatics

UR - https://2018.packet.video/

M3 - Paper

T2 - 23rd Packet Video Workshop 2018

Y2 - 12 June 2018 through 15 June 2018

ER -

An evaluation of video quality assessment metrics for passive gaming video streaming

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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An evaluation of video quality assessment metrics for passive gaming video streaming

Cite this