With the emergence of light field (LF) imaging technology, various challenging problems for LF visualization, compression, and transmission have to be addressed. Measuring perceptual quality is of utmost importance to assess the impact of an LF processing step on the visual experience of the finally rendered content to the end-user. In particular, objective quality assessment (QA) plays a key role in the quality optimization of LF imaging systems. In this paper, we conducted a comprehensive experiment to evaluate the performance of different objective QA methods for LF application. To this end, we selected a total number of 250 LFs (more than 48000 perspective views) from three public data sets to evaluate 16 objective QA metrics. Moreover, the subjective scores from three test data sets were aligned to produce an integrated data set for quality evaluation. The performance results across the different data sets aid to choose the most reliable metrics that are consistently performing well under various distortion and content characteristic conditions.
Mahmoudpour, S & Schelkens, P 2020, Cross Data Set Performance Consistency of Objective Quality Assessment Methods for Light Fields. in 2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020., 9123130, 2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 12th International Conference on Quality of Multimedia Experience, QoMEX 2020, Athlone, Ireland, 26/05/20. https://doi.org/10.1109/QoMEX48832.2020.9123130
Mahmoudpour, S., & Schelkens, P. (2020). Cross Data Set Performance Consistency of Objective Quality Assessment Methods for Light Fields. In 2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020 (pp. 1-6). Article 9123130 (2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QoMEX48832.2020.9123130
@inproceedings{1907bde624954ea7b6327dce3dd64ef6,
title = "Cross Data Set Performance Consistency of Objective Quality Assessment Methods for Light Fields",
abstract = "With the emergence of light field (LF) imaging technology, various challenging problems for LF visualization, compression, and transmission have to be addressed. Measuring perceptual quality is of utmost importance to assess the impact of an LF processing step on the visual experience of the finally rendered content to the end-user. In particular, objective quality assessment (QA) plays a key role in the quality optimization of LF imaging systems. In this paper, we conducted a comprehensive experiment to evaluate the performance of different objective QA methods for LF application. To this end, we selected a total number of 250 LFs (more than 48000 perspective views) from three public data sets to evaluate 16 objective QA metrics. Moreover, the subjective scores from three test data sets were aligned to produce an integrated data set for quality evaluation. The performance results across the different data sets aid to choose the most reliable metrics that are consistently performing well under various distortion and content characteristic conditions.",
keywords = "correlation, encoding, Light fields, objective quality assessment, subjective experiment",
author = "Saeed Mahmoudpour and Peter Schelkens",
year = "2020",
month = may,
doi = "10.1109/QoMEX48832.2020.9123130",
language = "English",
isbn = "9781728159669",
series = "2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2020 12th International Conference on Quality of Multimedia Experience, QoMEX 2020",
address = "United States",
note = "12th International Conference on Quality of Multimedia Experience, QoMEX 2020 ; Conference date: 26-05-2020 Through 28-05-2020",
}