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A deep neural network for image quality assessment

 
: Bosse, S.; Maniry, D.; Wiegand, T.; Samek, W.

:

Karam, L. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2016. Proceedings : September 25-28, 2016, Phoenix Convention Center, Phoenix, Arizona, USA
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-4673-9962-3 (Print)
ISBN: 978-1-4673-9961-6 (Online)
S.3773-3777
International Conference on Image Processing (ICIP) <23, 2016, Phoenix/Ariz.>
Englisch
Konferenzbeitrag
Fraunhofer HHI ()

Abstract
This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the network on the LIVE database and achieve a linear Pearson correlation superior to state-of-the-art NR IQA methods. We also apply the network to the image forensics task of decoder-sided quantization parameter estimation and also here achieve correlations of r = 0.989.

: http://publica.fraunhofer.de/dokumente/N-444988.html