Quantization Method in Spatial Domain for Screen Content Video Compression

스크린 콘텐츠 영상 압축을 위한 화소 영역 양자화 방법

  • Nam, Jung-Hak (Dept. of Computer Engineering, Kwangwoon University) ;
  • You, Jong-Hun (Dept. of Computer Engineering, Kwangwoon University) ;
  • Sim, Dong-Gyu (Dept. of Computer Engineering, Kwangwoon University) ;
  • Oh, Seoung-Jun (Dept. of Electronics and Communications Engineering, Kwangwoon University)
  • Received : 2012.03.19
  • Accepted : 2012.05.07
  • Published : 2012.07.25

Abstract

Expanding services and productions for screen content videos recently, necessity of new compression techniques is emerging. The next-generation video coding standard is also considering specified coding tools for screen content videos, but it is still preliminary stage. In this paper, we investigate the characteristics of screen content videos for which we propose the quantization in spatial domain to improve coding efficiency. The proposed method directly employs quantization for residual signal without any transformations. The proposed method also applies adaptive coefficients prediction and in-loop filter for quantized residual signals in spatial domain based on the characteristics of screen content videos. As a results, the proposed method for the random access, the low-delay and the all-intra modes achieve bit-saving about 4.4%, 5.1%. and 4.9%, respectively.

최근 스크린 콘텐츠 영상에 대한 제작 및 서비스가 확대됨에 따라, 이를 위한 새로운 압축 기술의 필요성이 대두되고 있다. 차세대 비디오 압축 표준에서도 스크린 콘텐츠 영상을 위한 특별한 부호화 기술 표준을 고려하고는 있지만 아직까지는 초보적인 단계에 있다. 본 논문에서는 스크린 콘텐츠 영상의 특성을 파악하여 이러한 종류의 영상에 대한 부호화 성능 향상을 위한 화소 영역의 양자화 방법을 제안한다. 제안하는 양자화 방법은 차분 신호를 변환 부호화 하지 않고 직접 화소 영역에서 양자화를 수행하며, 스크린 콘텐츠의 특성에 기반을 두고 양자화된 차분 신호에 대한 적응적인 계수 예측 기술과 화소 영역 양자화를 위한 인-루프 필터를 적용한다. 실험 결과, 제안하는 방법은 임의 접근 모드, 저지연 모드와 인트라 모드에서는 각각 평균4.4%, 5.1%와 4.9%의 부호화 성능을 얻었다.

Keywords

References

  1. V. Athitsos, M. J. Swain, and C. Frankel, "Distinguishing photographs and graphics on the World Wide Web," Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 10-17, San Juan, PR, June, 1997.
  2. M. Roach, J. S. Mason, and M. Pawlewski, "Motion-based classification of cartoons," Proc. International Symposium on Intelligent Multi -media, Video and Speech Processing, pp. 146-149, Hong Kong, HK, May, 2001.
  3. F. Pan, J. Chen, and J. Huang, "Discriminating between photorealistic computer graphics and natural images using fractal geometry," Science in China Series F: Information Sciences, vol. 52, no. 2, pp. 329-337, Feb. 2009. https://doi.org/10.1007/s11432-009-0053-5
  4. T.-T. Ng, S.-F. Chang, "Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics," Technical report, ADVENT Technical Report #220-2006-6 Columbia University, Oct. 2004.
  5. R. Zhang and R. Wang, "Distinguishing photorealistic and computer graphics from natural images by imaging features and visual features," Electronics, Communications amd Control (ICECC), 2011 International Conf. on, pp. 226-229, Ningbo, CN, Sept. 2011.
  6. T. Wiegand, W. Han, B. Bross, J. Ohm, and G. Sullivan, "WD5: Working Draft 5 of High- Efficiency Video Coding," Document of Joint Collaborative Team on Video Coding, JCTVC-G1103, Geneva, CH, Nov. 2011.
  7. F. Bossen, "Common test conditions and software reference configurations," Document of Joint Collaborative Team on Video Coding, JCTVC-G1200, Geneva, CH, Nov. 2011.
  8. 심동규, 조현호, 남정학, "HEVC (High Efficiency Video Coding) 최신 표준화 동향," 한국 멀티미디어학회지, 제 14권, 제 2호, pp. 1-15, 8월, 2011.
  9. 김휘용, 임성창, 이진호, 최진수, "HEVC 표준화 동향 및 요구사항," 전자공학회지, 제 38권, 제 8호, 15-21쪽, 2011년 8월.
  10. Y. Ye and M. Karczewicz, "Improved Intra Coding," ITU-T Q.6/SG16, Contribution C257, Geneva, CH, Jun. 2007.
  11. Y. Ye and M. Karczewicz, "Improved Intra Coding," ITU-T Q.6/SG16, VCEG-AG11, Shenzhen, CN, Oct. 2007.
  12. S. Lim, D. Kim, S. Jeong, J. Choi, H. Choi, and Y. Lee, "Rate-distortion optimized Adaptive transform coding," Optical Engineering, vol. 48, no. 8, pp. 087004 (1-14), Aug. 2009.
  13. 임성창, 김대연, 이영렬, "잔여 신호의 상관성에 기반한 선택 변환," 전자공학회논문지-SP편, 제 45 권, 제 2호, 37-48쪽, 2008년 3월.
  14. M. Narroschke and H. G. Musmann, "Adaptive prediction error coding in spatial and frequency domain with a fixed scan in the spatial domain," ITU-T SG.16/Q.6, VCEG-AD07, Hangzhou, CN, Oct. 2006.
  15. Y. Kim and D. Sim, "Adaptive Residual Coding in Spatial/Frequency Domains Based on Adaptive Spatial Quantization," International workshop on Advanced Image Technology (IWAIT 2008), Hsinchu, TW, Jan. 2008.
  16. J. Nam and D. Sim, "Lossless video coding based on pixel-wise prediction," Multimedia Systems, vol. 14, no. 5, pp. 291-298, Nov. 2008. https://doi.org/10.1007/s00530-008-0144-y
  17. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color Images," Proceedings of the Sixth International Conf. on Computer Vision (ICCV'98), pp. 839-846, Bombay, IN, Jan. 1998.
  18. http://hevc.kw.bbc.co.uk/trac/browser/tags/HM2.0, HM2.0 software.
  19. G. Bjontegaard, "Calculation of average PSNR differences between RD-Curves," ITU-T SG16/Q.6, VCEG-M33, Austin, TX, Apr. 2001.