NDLI: Blind distortion classification using content and perception based features

Content Provider	IEEE Xplore Digital Library
Author	Praneeth, D. Venkatanath, N. Bh, M.C. Channappayya, S.S. Medasani, S.S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol. Hyderabad, Hyderabad, India (Channappayya, S.S.) \|\| Image Understanding Group, Uurmi Syst. Pvt. Ltd., Hyderabad, India (Praneeth, D.; Venkatanath, N.; Bh, M.C.; Medasani, S.S.)
Abstract	We propose a novel COntent & Perception based features for DIstortion Classification (COPDIC) that can be used for efficient prediction of different distortions that are present in real world imagery. Unlike existing statistical methods, our approach uses human perception to derive features from local block level characteristics to classify common distortion types in images. Given an image with distortions, this paper presents features and a classification methodology that can be used to accurately predict the distortion type (like JPEG, Blur, JP2K, White Noise). The reported classification accuracies compete well with the state-of-the-art techniques for LIVE IQA, TID & CSIQ databases. The proposed technique has low computational complexity and can be employed for real-time applications.
Starting Page	71
Ending Page	75
File Size	601028
Page Count	5
File Format	PDF
ISBN	9781479965366
DOI	10.1109/QoMEX.2014.6982298
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Image quality Accuracy Databases Transform coding Distortion classification Standards No reference image quality
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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