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Perceptual video quality estimation by regression with myopic experts

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Perceptual video quality estimation by regression with myopic experts. / Giotsas, Vasileios; Deligiannis, Nikos; Fisher, Pam; Andreopoulos, Yiannis.

2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1-6 7148115.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Giotsas, V, Deligiannis, N, Fisher, P & Andreopoulos, Y 2015, Perceptual video quality estimation by regression with myopic experts. in 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015., 7148115, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015, Costa Navarino, Messinia, Greece, 26/05/15. https://doi.org/10.1109/QoMEX.2015.7148115

APA

Giotsas, V., Deligiannis, N., Fisher, P., & Andreopoulos, Y. (2015). Perceptual video quality estimation by regression with myopic experts. In 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015 (pp. 1-6). [7148115] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QoMEX.2015.7148115

Vancouver

Giotsas V, Deligiannis N, Fisher P, Andreopoulos Y. Perceptual video quality estimation by regression with myopic experts. In 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1-6. 7148115 https://doi.org/10.1109/QoMEX.2015.7148115

Author

Giotsas, Vasileios ; Deligiannis, Nikos ; Fisher, Pam ; Andreopoulos, Yiannis. / Perceptual video quality estimation by regression with myopic experts. 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1-6

Bibtex

@inproceedings{ce2a08a4ae574ab792b602c27b3bed27,
title = "Perceptual video quality estimation by regression with myopic experts",
abstract = "Objective video quality metrics can be viewed as 'myopic' expert systems that focus on particular aspects of visual information in video, such as image edges or motion parameters. We conjecture that the combination of many such high-level metrics leads to statistically-significant improvement in the prediction of reference-based perceptual video quality in comparison to each individual metric. To examine this hypothesis in a systematic and rigorous manner, we use: (i) the LIVE and the EPFL/PoliMi databases that provide the difference mean opinion scores (DMOS) for several video sequences under encoding and packet-loss errors; (ii) ten well-known metrics that range from mean-squared error based criteria to sophisticated visual quality estimators; (iii) five variants of regression-based supervised learning. For 400 experimental trials with random (non-overlapping) estimation and prediction subsets taken from both databases, we show that the best of our regression methods: (i) leads to statistically-significant improvement against the best individual metrics for DMOS prediction for more than 97% of the experimental trials; (ii) is statistically-equivalent to the performance of humans rating the video quality for 36.75% of the experiments with the EPFL/PoliMi database. On the contrary, no single metric achieves such statistical equivalence to human raters in any of the experimental trials.",
keywords = "objective metrics, perceptual video quality, supervised learning",
author = "Vasileios Giotsas and Nikos Deligiannis and Pam Fisher and Yiannis Andreopoulos",
year = "2015",
month = jul
day = "2",
doi = "10.1109/QoMEX.2015.7148115",
language = "English",
pages = "1--6",
booktitle = "2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015 ; Conference date: 26-05-2015 Through 29-05-2015",

}

RIS

TY - GEN

T1 - Perceptual video quality estimation by regression with myopic experts

AU - Giotsas, Vasileios

AU - Deligiannis, Nikos

AU - Fisher, Pam

AU - Andreopoulos, Yiannis

PY - 2015/7/2

Y1 - 2015/7/2

N2 - Objective video quality metrics can be viewed as 'myopic' expert systems that focus on particular aspects of visual information in video, such as image edges or motion parameters. We conjecture that the combination of many such high-level metrics leads to statistically-significant improvement in the prediction of reference-based perceptual video quality in comparison to each individual metric. To examine this hypothesis in a systematic and rigorous manner, we use: (i) the LIVE and the EPFL/PoliMi databases that provide the difference mean opinion scores (DMOS) for several video sequences under encoding and packet-loss errors; (ii) ten well-known metrics that range from mean-squared error based criteria to sophisticated visual quality estimators; (iii) five variants of regression-based supervised learning. For 400 experimental trials with random (non-overlapping) estimation and prediction subsets taken from both databases, we show that the best of our regression methods: (i) leads to statistically-significant improvement against the best individual metrics for DMOS prediction for more than 97% of the experimental trials; (ii) is statistically-equivalent to the performance of humans rating the video quality for 36.75% of the experiments with the EPFL/PoliMi database. On the contrary, no single metric achieves such statistical equivalence to human raters in any of the experimental trials.

AB - Objective video quality metrics can be viewed as 'myopic' expert systems that focus on particular aspects of visual information in video, such as image edges or motion parameters. We conjecture that the combination of many such high-level metrics leads to statistically-significant improvement in the prediction of reference-based perceptual video quality in comparison to each individual metric. To examine this hypothesis in a systematic and rigorous manner, we use: (i) the LIVE and the EPFL/PoliMi databases that provide the difference mean opinion scores (DMOS) for several video sequences under encoding and packet-loss errors; (ii) ten well-known metrics that range from mean-squared error based criteria to sophisticated visual quality estimators; (iii) five variants of regression-based supervised learning. For 400 experimental trials with random (non-overlapping) estimation and prediction subsets taken from both databases, we show that the best of our regression methods: (i) leads to statistically-significant improvement against the best individual metrics for DMOS prediction for more than 97% of the experimental trials; (ii) is statistically-equivalent to the performance of humans rating the video quality for 36.75% of the experiments with the EPFL/PoliMi database. On the contrary, no single metric achieves such statistical equivalence to human raters in any of the experimental trials.

KW - objective metrics

KW - perceptual video quality

KW - supervised learning

U2 - 10.1109/QoMEX.2015.7148115

DO - 10.1109/QoMEX.2015.7148115

M3 - Conference contribution/Paper

AN - SCOPUS:84939524131

SP - 1

EP - 6

BT - 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015

Y2 - 26 May 2015 through 29 May 2015

ER -