Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Attentional mechanisms for video retargeting and 3D compressive processing

비디오 재설정 및 3D 압축처리를 위한 어텐션 메커니즘

  • 황재정 (군산대학교 전파공학과)
  • Received : 2011.03.01
  • Accepted : 2011.03.19
  • Published : 2011.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we presented an attention measurement method in 2D and 3D image/video to be applied for image and video retargeting and compressive processing. 2D attention is derived from the three main components, intensity, color, and orientation, while depth information is added for 3D attention. A rarity-based attention method is presented to obtain more interested region or objects. Displaced depth information is matched to attention probability in distorted stereo images and finally a stereo distortion predictor is designed by integrating low-level HVS responses. As results, more efficient attention scheme is developed from the conventional methods and performance is proved by applying for video retargeting.

이 논문에서는 2D 및 3D 영상의 어텐션량을 측정하여 정지 및 동영상의 재설정 및 압축처리 기법을 제시하였다. 2D 어텐션은 세 개의 주요 구성, 즉, 영상의 세기, 컬러 및 방향성을 고려하였으며, 3D 영상에서 깊이 정보를 고려하였다. 시각적 어텐션은 관심있고 흥미있는 영역이나 객체를 검출하기 위해 희소성을 정량화하는 기법에 의해 구하였다. 왜곡된 스테레오 영상에서 변화된 깊이 정보를 어텐션 확률에 정합시켜서 최종적으로 저위 HVS 반응을 실제 어텐션 확률과 종합하여 스테레오 왜곡 예측기를 설계하였다. 결과로 기존 모델에 비해 효과적인 어텐션 기법을 개발하였으며 이를 비디오 재설정에 적용하여 성능을 입증하였다.

Keywords

References

  1. A. Kubota, et al. "Multiview imaging and 3DTV," IEEE Signal Processing Mag.,vol.24, no.6, pp.10-21, Nov. 2007.
  2. L. M. J. Meesters, W.A. IJsselsteijn, and P. J. H. Seuntiens, "A survey of perceptual evaluations and requirements of three-dimensional TV," IEEE Trans. on Circuits and Systems for Video Technol., vol. 14, no. 3, pp. 381-391, Mar. 2004. https://doi.org/10.1109/TCSVT.2004.823398
  3. M.H. Pinson and S. Wolf, "A new standardized method for objectively measuring video quality", IEEE Trans. on Broadcasting, vol.50, no. 3, pp.312-322, Sep. 2004. https://doi.org/10.1109/TBC.2004.834028
  4. F. Yang, S. Wan, Q. Xie and H.R. Wu, "No-reference quality assessment for networked video via primary analysis of bit stream", IEEE Trans. on Circuits and Systems for Video Technology, 2010.
  5. ITU-T, Recommendation P.910, Subjective video quality assessment methods for multimedia applications, April 2008.
  6. S. Narkhede and F. Golshani, "Stereoscopic imaging: a real-time, in depth look," IEEE Potentials, vol. 23, no. 1, pp. 38-42, Feb.-Mar. 2004. https://doi.org/10.1109/MP.2004.1266940
  7. G. Sun and N.S. Holliman, "Evaluating methods for controlling depth perception in stereoscopic cinematography", Stereoscopic Displays and Virtual Reality Systems XX, Proc. of SPIE-IS&T Electronic Imaging, SPIE, vol. 7237, Jan. 2009.
  8. [Awawdeh03] A. Awawdeh and G. Fan, "Pseudocepstrum for assessing stereo quality of retinal images," Asilomar Conf. on Signals, Systems and Computers, vol. 2, pp. 1953-1957, 9-12 Nov. 2003.
  9. M. Ferre, R. Aracil, and M. Sanchez-Uran, "Stereoscopic human interfaces," IEEE Robotics & Automation Mag., vol. 15, no. 4, pp. 50-57, Dec. 2008. https://doi.org/10.1109/MRA.2008.929929
  10. W.A. IJsselsteijn, H. de Ridder, J. Vliegen, "Subjective evaluation of stereoscopic images: effects of camera parameters and display duration," IEEE Trans. on Circuits and Systems for Video Technol., vol. 10, no. 2, pp. 225-233, Mar. 2000. https://doi.org/10.1109/76.825722
  11. Y. Zhai and M. Shah, "Visual attention detection in video sequences using spatiotemporal cues," Proc. the 14th ACM Int. Conf. on Multimedia, pp. 815-824, Dec. 2006.
  12. R. Milanese, H. Wechsler, S. Gill, J. M. Bots and T. Pun, "Integration of bottom-up and top-down cues for visual attention using non-linear relaxation," Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Seattle, pp. 781-785, 1994.
  13. L.Q. Chen, et al., "A visual attention model for adapting images on small displays," Multimedia Systems, vol. 9, pp. 353-364, 2003. https://doi.org/10.1007/s00530-003-0105-4
  14. L. Zhang, et al., "Regions of interest extraction based on visual attention model and watershed segmentation," 10th IEEE Int. Symp. on Multimedia (ISM'08), pp. 667-672, 15-17, Dec. 2008.
  15. L. Itti and C. Koch, "Computational modeling of visual attention," Nature Rev. Neuroscience, vol. 2, no. 11, pp. 194-203, Mar. 2001. https://doi.org/10.1038/35058500
  16. Q. Li, S. Wang, and X. Zhang, "Hierarchical identification of visually salient image regions," Int. Conf. on Audio, Language and Image Process. (ICALIP'08), pp. 1708-1712, 7-9 July 2008.
  17. A.A. Salah, E. Alpaydin, and L. Akarun, "A Selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 420-425, Mar. 2002. https://doi.org/10.1109/34.990146
  18. N. Ouerhani, et al., "Adaptive color image compression based on visual attention," 11th Int. Conf. on Image Analysis and Process., pp. 416-421, 26-28 Sept. 2001.
  19. Y.-F. Ma, L. Lu, H.-J. Zhang and M. Li, "A user attention model for video summarization," Proc. the Tenth ACM Int. Conf. on Multimedia, pp. 533-542, Dec. 2002.
  20. Y. Zhang, et al., "Stereoscopic visual attention model for 3D video," LNCS., vol. 5916, pp. 314-324, Dec. 2009.
  21. M. Mancas, B. Gosselin, and B. Macq, "A three-level computational attention model," Proc. of ICVS Workshop on Comput. Attention & Appl., 2007.
  22. S. Daly, "The visible differences predictor: An algorithm for the assessment of image fidelity," Digital Image and Human Vision, Cambridge, MIT press, A. Watson, ed., pp. 179-206, 1993.
  23. C. Zitnick and T. Kanade, A cooperative algorithm for stereo matching and occlusion detection, Robotics Institute Tech. Report, CMU-RI-TR-99-35, Carnegie Mellon University, Oct. 1999.