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Neural Network-Based Estimation of Distortion Sensitivity for Image Quality Prediction

: Bosse, S.; Becker, S.; Fisches, Z.V.; Samek, W.; Wiegand, T.


Institute of Electrical and Electronics Engineers -IEEE-:
25th IEEE International Conference on Image Processing, ICIP 2018 : October 7-10, 2018, Athens, Greece
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-4799-7061-2
ISBN: 978-1-4799-7062-9
International Conference on Image Processing (ICIP) <25, 2018, Athens>
Conference Paper
Fraunhofer HHI ()

Due to its computational simplicity, the PSNR is a popular and widely used image quality measure, although it correlates poorly with perceived visual quality. Distortion sensitivity, a reference image specific property, can be used to compensate for the lack of perceptual relevance of the PSNR. Based on the functional mapping between perceptual and computational quality a deep convolutional neural network is used to estimate patchwise distortion sensitivity. The local estimates are used for an imagewise perceptual adaptation of the PSNR. The performance of the proposed estimation approach is evaluated on the LIVE and TID2013 databases and shows comparable or superior performance as compared to benchmark image quality measures.