DOI QR코드

DOI QR Code

Enhanced High Contrast Image Rendering Method Using Visual Properties for Sharpness Perception

시각 선명도 감각 특성을 이용한 개선된 고명암 대비 영상 렌더링 기법

  • 이근영 (경북대학교 대학원 전자공학부, 오디오 비디오 신호처리 및 자동차 전자공학 연구실) ;
  • 이성학 (경북대학교 IT대학 전자공학부) ;
  • 권혁주 (경북대학교 대학원 전자공학부, 오디오 비디오 신호처리 및 자동차 전자공학 연구실) ;
  • 송규익 (경북대학교 IT대학 전자공학부, 오디오 비디오 신호처리 및 자동차 전자공학 연구실)
  • Received : 2013.05.24
  • Accepted : 2013.08.08
  • Published : 2013.08.30

Abstract

When an image is converted from HDR (high dynamic range) to LDR (low dynamic range), a tone mapping process is the essential component. Many TMOs (tone mapping operators) have been motivated by human vision which has lower physical luminance range than that in real scene. The representative of human vision properties which motivate TMOs is the local adaptation. However, TMOs are ultimately compressing image information such as contrast, saturation, etc. and the compression causes defects in image quality. In this paper, in order to compensate the degradation of the image which is caused by TMOs, the visual acuity-based edge stop function is proposed for applying the property of human vision to base-detail separation. In addition, using CSF (contrast sensitivity function) which represents the relationship among spatial frequency, contrast sensitivity, and luminance, the sharpness filter is designed and adaptively applied to the detail layer in regard to surround luminance.

HDR (high dynamic range) 영상을 LDR (low dynamic range) 영상으로 변환할 때 톤 맵핑 (tone mapping) 과정은 필수적이다. 많은 TMO (tone mapping operator)는 인간 시각 시스템의 특성들을 모방하여 발달되어 왔고 그 중 가장 대표적인 시각 특성이 국부 순응 방식이다. 그러나 TMO는 밝기나 명암, 채도 등의 영상 정보들을 압축하여 LDR 영상으로 대응시키기 때문에 압축에 의한 화질 저하가 나타난다. 본 논문에서는 TMO에 의한 화질 저하 보상을 위해 인간 시각의 선명도 특성을 기저 및 세부 영상 분할 처리에 적용하여 휘도 적응적 에지 보존 함수를 제안했다. 또한, 인간 시각 시스템에서 공간 주파수와 대비 민감도 사이의 관계를 나타내는 CSF (contrast sensitivity function)를 이용하여 선명화 필터를 설계하고, 이를 배경 휘도에 따라 적응적으로 적용하였다.

Keywords

References

  1. J. Kuang, G. Johnson, and M. Fairchild, "iCAM06: a refined image appearance model for HDR image rendering," J. Visual Commun. Image Representation, vol. 18, no. 5, pp. 406-414, June 2007. https://doi.org/10.1016/j.jvcir.2007.06.003
  2. P. Ledda, A. Chalmers, T. Troscianko, and H. Seetzen, "Evaluation of tone mapping operators using a high dynamic range display," ACM Trans. Graphics, vol. 24, no. 3, pp. 640-648, July 2005. https://doi.org/10.1145/1073204.1073242
  3. E. Reinhard, G. Ward, S. Pattanaik, and P. Devevec, High Dynamic range imaging : acquisition, display and Image-Based Lighting, Morgan Kaufmann, 2005.
  4. M. Fairchild, Color Appearance Model, 2nd Ed., John Wiley&Sons, Ltd, 2005.
  5. F. Durand and J. Dorsey, "Fast bilateral filtering for the display of high-dynamic-range images," ACM Trans. Graphics, vol. 21, no. 3, pp. 257-266, July 2002.
  6. H. G. Kim, S. H. Lee, T. W. Bae, and K. I. Sohng, "Color saturation compensation in iCAM06 for high-chroma HDR imaging," IEICE Trans. Fundam. Electron., Commun., Comput. Sci., vol. E94-A, no. 11, pp. 2353-2357, Nov. 2011. https://doi.org/10.1587/transfun.E94.A.2353
  7. S. M. Chae, S. H. Lee, H. J. Kwon, and K. I. Sohng, "A tone compression model for the compensation of white point shift generated from HDR rendering," IEICE Trans. Fundam. Electron. Commun. Comput. Sci., vol. E95-A, no. 8, pp. 1297-1301, Aug. 2012. https://doi.org/10.1587/transfun.E95.A.1297
  8. H. J. Kwon, S. H. Lee, S. C. Chae, and K. I. Sohng, "Multi scale tone mapping model using visual brightness functions for HDR image compress," J. Commun. Networks (JCN), vol. 37A, no. 12, pp. 1054-1064, Dec. 2012.
  9. J. A. Ferwerda, S. N. Pattanaik, P. Shirley, and D. P. Greenberg, "A model of visual adaptation for realistic image synthesis," in Proc. 23rd Annu. Conf. Comput. Graphics Interactive Techniques (SIGGRAPH '96), pp. 249-258, New Orleans, U.S.A., Aug. 1996.
  10. S. Shaler, "The relation between visual acuity and illumination," J. General Physiology, vol. 21, no. 2, pp. 165-188, Nov. 1937. https://doi.org/10.1085/jgp.21.2.165
  11. P. G. J. Barten, Contrast sensitivity of the human eye and its effects on image quality, SPIE Optical Engineering Press, 1999.
  12. A. van Meeteren and J. J. Vos, "Resolution and contrast sensitivity at low luminance levels," Vision Research, vol. 12, no. 5, pp. 825-833, May 1972. https://doi.org/10.1016/0042-6989(72)90008-9
  13. S. Westland, H. Owens, V. Cheung, and I. Paterson-Stephens, "Model of luminance contrast-sensitivity function for application to image assessment," Color Research Applicat., vol. 31, no. 4, pp. 315-319, Aug. 2006. https://doi.org/10.1002/col.20230
  14. D. J. Jobson, Z. Rahman, G. A. Woodell, and G. D. Hines, "A comparison of visual statistics for the image enhancement of FORESITE aerial images with those of major image classes," Proc. SPIE, Visual Inform. Process. XV, vol. 6246, Article no. 624601, May 2006.