(Received: 2019-07-27, Revised: 2019-10-06 , Accepted: 2019-10-21)
Due to technology advances in multimedia, larger storage spaces, large internet bandwidth and high-transmission speed are required for the transmission of videos. Video compression techniques play a vital role in reducing video size; therefore, smaller storage space and lower internet bandwidth are eventually required. In this paper, the EEG signal is used to modify the compression ratio of videos based on the interest of the viewer. This is performed by associating the compression ratio applied to the video with the degree of interest using a group of frames. This interest for a group of frames is measured using the EEG signal to demonstrate the viewer responses to videos. Statistical techniques applied to the EEG signal (such as peaks-over-threshold and time-of-peaks-over-thresholds) are used to extract the frames of interest. Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and Mean-Square Error (MSE) are used to compare the performance of the proposed technique with the MPEG-4 technique. The results show a reduction of 15 % in the video size compared with the MPEG-4 technique without deteriorating the quality of the videos.
  1. N. Memon and K. Sayood, "Lossless Compression of Video Sequences," IEEE Trans. Commun., vol. 44, no. 10, pp. 1340–1345, 1996.
  2. O. Avaro, A. Eleftheriadis, C. Herpel, G. Rajan and L. Ward, "MPEG-4 Systems: Overview," Signal Process. Image Commun., vol. 15, no. 4–5, pp. 281–298, 2000.
  3. K. Rijkse and K. Research, "H.263: Video Coding for Low-Bit-Rate Communication," IEEE Communications Magazine, pp. 42–45, 1996.
  4. S. Ponlatha and R. S. Sabeenian, "Comparison of Video Compression Standards," Int. J. Comput. Electr. Eng., vol. 5, no. 6, pp. 549–554, 2013.
  5. J. G. Webster, Medical Instrumentation: Application and Design, 4th Ed., Wiley, 2010.
  6. S. Ki, S. H. Bae, M. Kim and H. Ko, "Learning-based Just-noticeable-quantization-distortion Modeling for Perceptual Video Coding," IEEE Trans. on Image Processing, vol. 27, no. 7, pp.3178-3193, 2018.
  7. Prangnell, Lee and V. Sanchez, "JND-based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC," Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 1203-1207, 2017.
  8. M. Takeuchi, S. Saika, Y. Sakamoto, T. Nagashima, Z. Cheng, K. Kanai, J. Katto, K. Wei, J. Zengwei and X. Wei, "Perceptual Quality Driven Adaptive Video Coding Using JND Estimation," Picture Coding Symposium (PCS), 179-183, 2018.
  9. Helmrich, R. Christian et al., "Perceptually Optimized Bit-allocation and Associated Distortion Measure for Block-based Image or Video Coding," Data Compression Conference (DCC), pp.172-181, 2019.
  10. Perez-Daniel, R. Karina and V. Sanchez, "Luma-aware Multi-model Rate-control for HDR Content in HEVC," Proc. of IEEE International Conference on Image Processing (ICIP), pp. 1022-1026, 2017.
  11. G. J. Sullivan, J.-R. Ohm, W.-J. Han and T. Wiegand, "Overview of the High-Efficiency Video Coding (HEVC) Standard," IEEE Trans. on Circ. and Sys. for Video Tech., vol. 22, no. 12, pp. 1649-1668, 2012.
  12. E. Niedermeyer and F. Lopes da Silva, Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Lippincott Williams & Wilkins, 2005.
  13. M. Dimaki, P. Vazquez, M. H. Olsen, L. Sasso, R. Rodriguez-Trujillo, I. Vedarethinam and W. E. Svendsen, "Fabrication and Characterization of 3D Micro and Nanoelectrodes for Neuron Recordings," Sensors (Switzerland), vol. 10, no. 11, pp. 10339–10355, 2010.
  14. S. J. M. Smith, "EEG in the Diagnosis, Classification and Management of Patients with Epilepsy," Journal of Neurology, Neurosurgery and Psychiatry, vol. 76, Suppl. 2, pp. ii2-ii7, 2005.
  15. S. Scholler, S. Bosse, M. S. Treder, B. Blankertz, G. Curio, K.-R. Müller and T. Wiegand, "Toward a Direct Measure of Video Quality Perception Using EEG," IEEE Trans. Image Process., vol. 21, no. 5, pp. 2619–2629, 2012.
  16. L. Lindemann and M. Magnor, "Assessing the Quality of Compressed Images Using EEG," Proc. of the 18th IEEE Int. Conf. Image Process., pp. 3109–3112, 2011.
  17. H.-C. Li, J. Seo, K. Kham and S. Lee, "Measurement of 3D Visual Fatigue Using Event-related Potential (ERP): 3D Oddball Paradigm," Proceedings of 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video, pp. 213–216, 2008.
  18. L. Acqualagna, S. Bosse, A. K. Porbadnigk, G. Curio, K.-R. Müller, T. Wiegand and B. Blankertz, "EEG-based Classification of Video Quality Perception Using Steady State Visual Evoked Potentials (SSVEPs)," Jour. Neural Eng., vol. 12, no. 2, p. 26012, 2015.
  19. C. Tan, F. Sun and W. Zhang. "Deep Transfer Learning for EEG-based Brain Computer Interface," Proc of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  20. U. Engelke, D. P. Darcy, G. H. Mulliken, S. Bosse, M. G. Martini, S. Arndt, J. Antons, K. Y. Chan, N. Ramzan and K. Brunnström, "Psychophysiology-based QoE Assessment: A Survey," IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 1, pp. 6-21, 2017.
  21. S. Bosse, K. Müller, T. Wiegand and W. Samek, "Brain-Computer Interfacing for Multimedia Quality Assessment," Proc. of IEEE International Conference on Systems, Man and Cybernetics (SMC), Budapest, pp. 002834-002839, 2016.
  22. S. Bosse, L. Acqualagna, W. Samek, A. Porbadnigk, G. Curio, B. Blankertz, K. Mueller and T. E. Wiegand, "Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 8, pp. 1694-1706, Aug. 2018.
  23. F. S. Avarvand, S. Bosse, K. Mueller, R. Schaefer, G. Nolte, T. E. Wiegand, G. Curio and W. Samek, "Objective Quality Assessment of Stereoscopic Images with Vertical Disparity Using EEG," Journal of Neural Engineering, vol. 14, no. 4, p. 046009, 2017.
  24. L. Jia, Y. Tu, L. Wang, X. Zhong and Y. Wang, "Study of Image Quality Using Event-related Potentials Measurement," Jour. Electronic Imaging, vol. 27, p. 033046, 2018.
  25. L. Jia, L. Wang, Y. Tu and X. Zhong, "Studying the Effect of ROI on Image Quality Using ERPS," Proc. of the 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC), pp. 829-833, 2018.
  26. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.
  27. S. Bosse, K. Brunnström, S. Arndt, M. G. Martini and N. Ramzan, "A Common Framework for the Evaluation of Psychophysiological Visual Quality Assessment," Quality and User Experience, vol. 4, no. 3,[Online], Available:
  28. S. Arndt, J. Antons, R. Schleicher, S. Möller and G. Curio, "Using Electroencephalography to Measure Perceived Video Quality," IEEE Jou. of Selected Topics in Signal Proc., vol. 8, no. 3, pp. 366-376, 2014.
  29. S. Scholler , S. Bosse , M. Treder , B. Blankertz , G. Curio , K. Muller and T. Wiegand, "Toward a Direct Measure of Video Quality Perception Using EEG," IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2619-2629, May 2012.
  30. S. Möller, S. Arndt, J. Antons, G. Curio, R. Schleicher and S. Scholler, "A Physiological Approach to Determine Video Quality," Proc. of IEEE International Symposium on Multimedia, Dana Point, California, USA, pp. 518-523, 2013.
  31. P. A. Abhang, B. W. Gawali and S. C. Mehrotra, Technological Basics of EEG Recording and Operation of Apparatus, Dec. 2016.
  32. Juri D. Kropotov, Quantitative EEG, Event-related Potentials and Neurotherapy, ISBN 978-0-12-374512-5 Academic Press, 2009.
  33. J. W. Britton, L. C. Frey, J. L. Hopp et al., Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children and Infants, American Epilepsy Society, ISBN-13: 978-0-9979756-0-4, 2016.
  34. R. B. Mfarij, Video Compression from EEG, M.Sc. Thesis, Department of Computer Engineering, Yarmouk University, Irbid, 2017.