A quad tree based method for blurred and non-blurred video text frames classification through quality metrics

Khare, V; Shivakumara, P; Kumar, A; Chan, CS; Lu, T; Blumenstien, M

A quad tree based method for blurred and non-blurred video text frames classification through quality metrics

Khare, V Shivakumara, P Kumar, A Chan, CS Lu, T Blumenstien, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2016, 0 pp. 4023 - 4028
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Khare, V	en_US
dc.contributor.author	Shivakumara, P	en_US
dc.contributor.author	Kumar, A	en_US
dc.contributor.author	Chan, CS	en_US
dc.contributor.author	Lu, T	en_US
dc.contributor.author	Blumenstien, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2016, 0 pp. 4023 - 4028	en_US
dc.identifier.isbn	9781509048472	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126296
dc.description.abstract	© 2016 IEEE. Blur is a common artifact in video, which adds more complexity to text detection and recognition. To achieve good accuracies for text detection and recognition, this paper suggests a new method for classifying blurred and non-blurred frames in video. We explore quality metrics, namely, BRISQUE, NRIQA, GPC and SI, in a new way for classification. We estimate the values of these metrics with the help of predefined samples called reference values. To widen the difference between metric values for better classification, we introduce scaling factors as a non-linear sigmoidal function, which considers the metric of each current frame and its reference and results in templates. Based on the characteristics of metrics, the proposed method finds a relationship between the metrics to derive rules for classification. To classify the frame containing local blur, we explore quad tree division with classification rules which divide non-blurred blocks to identify local blur. We use standard databases, namely, ICDAR 2013, ICDAR 2015 and YVT videos for experimentation, and evaluate the proposed method in terms of text detection and recognition rates given by text detection and binarization methods before and after classification.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/ICPR.2016.7900263	en_US
dc.title	A quad tree based method for blurred and non-blurred video text frames classification through quality metrics	en_US
dc.type	Conference Proceeding
utslib.citation.volume	0	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	0	en_US

Abstract:

© 2016 IEEE. Blur is a common artifact in video, which adds more complexity to text detection and recognition. To achieve good accuracies for text detection and recognition, this paper suggests a new method for classifying blurred and non-blurred frames in video. We explore quality metrics, namely, BRISQUE, NRIQA, GPC and SI, in a new way for classification. We estimate the values of these metrics with the help of predefined samples called reference values. To widen the difference between metric values for better classification, we introduce scaling factors as a non-linear sigmoidal function, which considers the metric of each current frame and its reference and results in templates. Based on the characteristics of metrics, the proposed method finds a relationship between the metrics to derive rules for classification. To classify the frame containing local blur, we explore quad tree division with classification rules which divide non-blurred blocks to identify local blur. We use standard databases, namely, ICDAR 2013, ICDAR 2015 and YVT videos for experimentation, and evaluate the proposed method in terms of text detection and recognition rates given by text detection and binarization methods before and after classification.

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http://hdl.handle.net/10453/126296