3D visual discomfort predictor based on neural activity statistics

Heeseok Oh, Jongyoo Kim, Sanghoon Lee, Alan C. Bovik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Visual discomfort assessment (VDA) on stereoscopic images is of fundamental importance for making decisions regarding visual fatigue caused by unnatural binocular alignment. Nevertheless, no solid framework exists to quantify this discomfort using models of the responses of visual neurons. Binocular vision is realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. We propose a neuronal model-based framework called Neural 3D Visual Discomfort Predictor (N3D-VDP) that automatically predicts the level of visual discomfort experienced when viewing stereoscopic 3D (S3D) images. The N3D-VDP model extracts features derived by estimating the neural activity associated with the processing of binocular disparities. In this regard we deploy a model of disparity processing in the extra-striate middle temporal (MT) region of occipital lobe. We compare the performance of N3D-VDP with other recent VDA algorithms using correlations against reported subjective visual discomfort, and show that N3D-VDP is statistically superior to the other methods.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages3560-3564
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 2015 Dec 9
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sep 272015 Sep 30

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period15/9/2715/9/30

Fingerprint

Statistics
Binoculars
Binocular vision
Eye movements
Processing
Neurons
Decision making
Fatigue of materials

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Oh, H., Kim, J., Lee, S., & Bovik, A. C. (2015). 3D visual discomfort predictor based on neural activity statistics. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 3560-3564). [7351467] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351467
Oh, Heeseok ; Kim, Jongyoo ; Lee, Sanghoon ; Bovik, Alan C. / 3D visual discomfort predictor based on neural activity statistics. 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. pp. 3560-3564 (Proceedings - International Conference on Image Processing, ICIP).
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Oh, H, Kim, J, Lee, S & Bovik, AC 2015, 3D visual discomfort predictor based on neural activity statistics. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings., 7351467, Proceedings - International Conference on Image Processing, ICIP, vol. 2015-December, IEEE Computer Society, pp. 3560-3564, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 15/9/27. https://doi.org/10.1109/ICIP.2015.7351467

3D visual discomfort predictor based on neural activity statistics. / Oh, Heeseok; Kim, Jongyoo; Lee, Sanghoon; Bovik, Alan C.

2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. p. 3560-3564 7351467 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Oh H, Kim J, Lee S, Bovik AC. 3D visual discomfort predictor based on neural activity statistics. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society. 2015. p. 3560-3564. 7351467. (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2015.7351467