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Neurally informed assessment of perceived natural texture image quality

 
: Bosse, S.; Acqualagna, L.; Porbadnigk, A.K.; Blankertz, B.; Curio, G.; Müller, K.-R.; Wiegand, T.

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Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2014. Proceedings. Vol.3 : Paris, France, 27 - 30 October 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-5751-4
ISBN: 978-1-4799-5752-1
pp.1987-1991
International Conference on Image Processing (ICIP) <21, 2014, Paris>
English
Conference Paper
Fraunhofer HHI ()

Abstract
Conventionally, the quality of images and related codecs are assessed using subjective tests, such as Degradation Category Rating. These quality assessments consider the behavioral level only. Recently, it has been proposed to complement this approach by investigating how quality is processed in the brain of a user (using electroencephalography, EEG), potentially leading to results that are less biased by subjective factors. In this paper, a novel method is presented for assessing how image quality is processed on a neural level, using Steady-State Visual Evoked Potentials (SSVEPs) as EEG features. We tested our approach in an EEG study with 16 participants who were presented with distorted images of natural textures. Subsequently, we compared our approach analogously to the standardized Degradation Category Rating quality assessment. Remarkably, our novel method yields a correlation of r = 0.93 to MOS on the recorded dataset.

: http://publica.fraunhofer.de/documents/N-351134.html