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Social image quality

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Social image quality. / Qiu, Guoping; Kheiri, Ahmed.
Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII. Vol. 7867 2011. 78670S.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Qiu, G & Kheiri, A 2011, Social image quality. in Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII. vol. 7867, 78670S, Image Quality and System Performance VIII, San Francisco, CA, United States, 24/01/11. https://doi.org/10.1117/12.872378

APA

Qiu, G., & Kheiri, A. (2011). Social image quality. In Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII (Vol. 7867). Article 78670S https://doi.org/10.1117/12.872378

Vancouver

Qiu G, Kheiri A. Social image quality. In Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII. Vol. 7867. 2011. 78670S doi: 10.1117/12.872378

Author

Qiu, Guoping ; Kheiri, Ahmed. / Social image quality. Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII. Vol. 7867 2011.

Bibtex

@inproceedings{189db1e46a574dec9df2737a0912c63e,
title = "Social image quality",
abstract = "Current subjective image quality assessments have been developed in the laboratory environments, under controlled-conditions, and are dependent on the participation of limited numbers of observers. In this research, with the help of Web 2.0 and social media technology, a new method for building a subjective image quality metric has been developed where the observers are the Internet users. A website with a simple user interface that enables Internet users from anywhere at any time to vote for a better quality version of a pair of the same image has been constructed. Users' votes are recorded and used to rank the images according to their perceived visual qualities. We have developed three rank aggregation algorithms to process the recorded pair comparison data, the first uses a naive approach, the second employs a Condorcet method, and the third uses the Dykstra's extension of Bradley-Terry method. The website has been collecting data for about three months and has accumulated over 10,000 votes at the time of writing this paper. Results show that the Internet and its allied technologies such as crowdsourcing offer a promising new paradigm for image and video quality assessment where hundreds of thousands of Internet users can contribute to building more robust image quality metrics. We have made Internet user generated social image quality (SIQ) data of a public image database available online (http://www.hdri.cs.nott.ac.uk/siq/) to provide the image quality research community with a new source of ground truth data. The website continues to collect votes and will include more public image databases and will also be extended to include videos to collect social video quality (SVQ) data. All data will be public available on the website in due course.",
keywords = "Crowd sourcing, Image and video quality, Image quality metric, Paired comparison, Psychometric, Rank aggregation, Social media, Web2.0",
author = "Guoping Qiu and Ahmed Kheiri",
year = "2011",
month = feb,
day = "25",
doi = "10.1117/12.872378",
language = "English",
isbn = "9780819484048",
volume = "7867",
booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII",
note = "Image Quality and System Performance VIII ; Conference date: 24-01-2011 Through 26-01-2011",

}

RIS

TY - GEN

T1 - Social image quality

AU - Qiu, Guoping

AU - Kheiri, Ahmed

PY - 2011/2/25

Y1 - 2011/2/25

N2 - Current subjective image quality assessments have been developed in the laboratory environments, under controlled-conditions, and are dependent on the participation of limited numbers of observers. In this research, with the help of Web 2.0 and social media technology, a new method for building a subjective image quality metric has been developed where the observers are the Internet users. A website with a simple user interface that enables Internet users from anywhere at any time to vote for a better quality version of a pair of the same image has been constructed. Users' votes are recorded and used to rank the images according to their perceived visual qualities. We have developed three rank aggregation algorithms to process the recorded pair comparison data, the first uses a naive approach, the second employs a Condorcet method, and the third uses the Dykstra's extension of Bradley-Terry method. The website has been collecting data for about three months and has accumulated over 10,000 votes at the time of writing this paper. Results show that the Internet and its allied technologies such as crowdsourcing offer a promising new paradigm for image and video quality assessment where hundreds of thousands of Internet users can contribute to building more robust image quality metrics. We have made Internet user generated social image quality (SIQ) data of a public image database available online (http://www.hdri.cs.nott.ac.uk/siq/) to provide the image quality research community with a new source of ground truth data. The website continues to collect votes and will include more public image databases and will also be extended to include videos to collect social video quality (SVQ) data. All data will be public available on the website in due course.

AB - Current subjective image quality assessments have been developed in the laboratory environments, under controlled-conditions, and are dependent on the participation of limited numbers of observers. In this research, with the help of Web 2.0 and social media technology, a new method for building a subjective image quality metric has been developed where the observers are the Internet users. A website with a simple user interface that enables Internet users from anywhere at any time to vote for a better quality version of a pair of the same image has been constructed. Users' votes are recorded and used to rank the images according to their perceived visual qualities. We have developed three rank aggregation algorithms to process the recorded pair comparison data, the first uses a naive approach, the second employs a Condorcet method, and the third uses the Dykstra's extension of Bradley-Terry method. The website has been collecting data for about three months and has accumulated over 10,000 votes at the time of writing this paper. Results show that the Internet and its allied technologies such as crowdsourcing offer a promising new paradigm for image and video quality assessment where hundreds of thousands of Internet users can contribute to building more robust image quality metrics. We have made Internet user generated social image quality (SIQ) data of a public image database available online (http://www.hdri.cs.nott.ac.uk/siq/) to provide the image quality research community with a new source of ground truth data. The website continues to collect votes and will include more public image databases and will also be extended to include videos to collect social video quality (SVQ) data. All data will be public available on the website in due course.

KW - Crowd sourcing

KW - Image and video quality

KW - Image quality metric

KW - Paired comparison

KW - Psychometric

KW - Rank aggregation

KW - Social media

KW - Web2.0

U2 - 10.1117/12.872378

DO - 10.1117/12.872378

M3 - Conference contribution/Paper

AN - SCOPUS:79951827591

SN - 9780819484048

VL - 7867

BT - Proceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance VIII

T2 - Image Quality and System Performance VIII

Y2 - 24 January 2011 through 26 January 2011

ER -