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

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. Base...

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Bibliographic Details
Main Authors: Bosse, Sebastian, Becker, Soren, Fisches, Zacharias V., Samek, Wojciech, Wiegand, Thomas
Format: Conference Proceeding
Language:English
Subjects:
Distortion
Distortion measurement
distortion sensitivity
Estimation
Image quality
image quality assessment
Neural network
perceptual models
Sensitivity
Training
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451261