DOI QR코드

DOI QR Code

Disparity Estimation for Intermediate View Reconstruction of Multi-view Video

다시점 동영상의 중간시점영상 생성을 위한 변이 예측 기법

  • Choi, Mi-Nam (Dept. of Electronics Engineering, Kwangwoon Univ.) ;
  • Yun, Jung-Hwan (Dept. of Electronics Engineering, Kwangwoon Univ.) ;
  • Yoo, Ji-Sang (Dept. of Electronics Engineering, Kwangwoon Univ.)
  • Published : 2008.11.30

Abstract

In this paper, we propose an algorithm for pixel-based disparity estimation with reliability in the multi-view image. The proposed method estimates an initial disparity map using edge information of an image, and the initial disparity map is used for reducing the search range to estimate the disparity efficiently. Furthermore, disparity-mismatch on object boundaries and textureless-regions get reduced by adaptive block size. We generated intermediate-view images to evaluate the estimated disparity. Test results show that the proposed algorithm obtained $0.1{\sim}1.2dB$ enhanced PSNR(peak signal to noise ratio) compared to conventional block-based and pixel-based disparity estimation methods.

본 논문은 다시점 카메라로부터 획득된 영상을 이용하여 영상내의 모든 화소에 대한 정확한 변이 정보를 구하는 알고리듬을 제안한다. 제안한 방법은 객체의 경계 정보를 고려하여 초기 변이를 예측한 후 획득된 변이 정보를 이용하여 탐색 범위를 줄임으로 써 효율적으로 변이를 예측한다. 또한 가변 블록을 사용하여 텍스쳐 정보가 부족한 영역과 경계부분에서 발생하는 오정합 문제를 줄일 수 있다. 획득된 변이 맵 정보를 이용하여 중간시점영상을 생성한 결과 기존의 블록기반 변이 추정방식과 화소기반의 변이 예측방식에 비해 $0.1dB{\sim}1.2dB$의 PSNR(Peak signal to noise ratio)이 향상되는 것을 확인하였다.

Keywords

References

  1. A Smolic, H Kimata, "Description of Exploration Experiments in 3DAV," ISO/IEC JTC1/SC29/WG11 N4929, July 2002
  2. J. S. McVeigh, "Efficient Compression of Arbitrary Multi-view Video Signal," Ph.D. dissertation, CMU, June 1996
  3. Stephen T. Barnard, and William B. Thompson, " Disparity Analysis of Image," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, pp. 333-340, July 1980 https://doi.org/10.1109/TPAMI.1980.4767032
  4. J. S. McVeigh, M. Siegel, and A. Jordan, "Intermediate View Synthesis Considering Occluded and Ambiguously Referenced Image Regions," Signal Processing Image Communication, vol. 9, pp. 21-28, Sep 1996 https://doi.org/10.1016/S0923-5965(96)00005-7
  5. J. Y. Goulermas and P. Liatsis, "Hybrid Symbiotic Genetic Optimization for Robust Edge-based Stereo Correspondence," Pattern Recognition, vol. 34, pp. 2477-2496, Dec 2001 https://doi.org/10.1016/S0031-3203(00)00163-1
  6. Yao Wang, and Ouseb Lee, "Use of 2-D Deformable Mesh Structures for Video Coding," IEEE Trans. Circuits and systems for video technology, vol. 6, pp. 636-646, Dec 1996 https://doi.org/10.1109/76.544735
  7. D. Tzovaras, N. Grammalidis, and M. G. Strintzis, "Object-based Coding of Stereo Image Sequences Using Joint 3-D Motion Disparity Compensation," IEEE Trans. Circuits and Systems for Video Technology, vol. 7, pp. 312-327, Apr 1997 https://doi.org/10.1109/76.564110
  8. Y. Ohta and T. Kanede, "Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, pp. 139-154, Mar 1985 https://doi.org/10.1109/TPAMI.1985.4767639
  9. S. D. Cochran and G. Medioni, "3-D Surface Description from Binocular Stereo," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 981-994, Oct 1992 https://doi.org/10.1109/34.159902
  10. R. Franich, "Disparity Estimation in Stereoscopic Digital Images," PhD thesis, Tech-. nical University of Delft, 1996
  11. R_Szeliski. "Prediction Error as a Quality Metric for Motion and Stereo," IEEE International Conference Computer Vision, Vol. 2, pp. 781-788, Sep 1999
  12. http://vision.middlebury.edu/stereo