Image Quality Assessment

Images and videos are ubiquitous today. The share of bits representing visual signals is huge and even growing. With decreasing bit rate, distortions are introduced into the transmitted signal that become visible for the human eye. In order to automatically evaluate and optimize the performance of transmission systems, a metric for image or video quality is necessary. As humans are typically the ultimate receiver of visual signals, it is crucial for such a metric to relate to human visual perception and to predict the visual distortion perceived by humans reliably.

We develop deep convolutional neural networks for no-reference and full-reference image quality assessment, which allows for joint learning of local quality and spatial attention, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Furthermore, we investigated the use of EEG technoloqy for extracting brain correlations for user’s perceived quality.

Publications

  1. S. Bosse, D. Maniry, K.-R. Müller, T. Wiegand, and W. Samek, "Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment", IEEE Transactions on Image Processing, September 2017.
  2. F. Shahbazi Avarvand, S. Bosse, K.-R. Müller, G. Nolte, T. Wiegand, R. Schäfer, G. Curio, and W. Samek, "Objective Quality Assessment of Stereoscopic Images with Vertical Disparity using EEG", Journal of Neural Engineering, IOP Publishing, vol. 14, no. 4, May 2017.
  3. S. Bosse, L. Acqualagna, W. Samek, A. K. Porbadnigk, G. Curio, B. Blankertz, K.-R. Müller, and T. Wiegand, "Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition", IEEE Transactions on Circuits and Systems for Video Technology, April 2017.
  4. S. Bosse, D. Maniry, T. Wiegand, and W. Samek, "A Deep Neural Network for Image Quality Assessment", Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, pp. 3773-3777, September 2016.
  5. S. Bosse, D. Maniry, K.-R. Müller, T. Wiegand, and W. Samek, "Neural Network-Based Full- Reference Image Quality Assessment", Proceedings of the Picture Coding Symposium (PCS), Nürnberg, Germany, pp. 1-5, December 2016.
  6. S. Bosse, K.-R. Müller, T. Wiegand, and W. Samek, "Brain-Computer Interfacing for Multimedia Quality Assessment", Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, pp. 002834-002839, October 2016.