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Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human visual system. We evaluate PerceptNet on various traditional perception datasets and note strong performance on a number of them as compared with traditional image quality metrics. We also show that including a nonlinearity inspired by the human visual system in classical deep neural networks architectures can increase their ability to judge perceptual similarity. Compared to similar deep learning methods, the performance is similar, although our network has a number of parameters that is several orders of magnitude less.
|Title of host publication||2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - Oct 2020|
|Event||2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates|
Duration: 25 Sep 2020 → 28 Sep 2020
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||2020 IEEE International Conference on Image Processing, ICIP 2020|
|Country||United Arab Emirates|
|City||Virtual, Abu Dhabi|
|Period||25/09/20 → 28/09/20|
Bibliographical noteFunding Information:
This work was partially funded by EPSRC grant EP/N509619/1 and MINECO grant DPI2017-89867.
© 2020 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- human visual system
- neural networks
- perceptual distance