Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Self-Organizing Model Based Rate Control Algorithm for MPEG-4 Video Coding

  • Published : 2003.01.01
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Abstract

A new self-organizing neuro-fuzzy network based rate control algorithm for MPEG-4 video encoder is proposed in this paper. Contrary to the traditional methods that construct the rate-distorion (RD) model based on experimental equations, the proposed method effectively exploits the non-stationary property of the video date with neuro-fuzzy network that self-organizes the RD model online and adaptively updates the structure. The method needs not require off-line pre-training; hence it is geared toward real-time coding. The comparative results through the experiments suggest that our proposed rate control scheme encodes the video sequences with less frame skip, providing good temporal quality and higher PSNR, compared to VM18.0.

본 논문에서는 자기구성 뉴로퍼지 네트워크를 이용한 MPEG-4 비트율 제어알고리즘을 제안한다. 경험적인 수식을 바탕으로 rate-distortion(RD) 모델을 구성하는 일반적인 방법과는 달리 제안하는 알고리즘의 기본적인 아이디어는 온라인으로 RD모델을 스스로 구성하고 매 프레임마다 그 구조를 적응적으로 업데이트하는 SOLPN을 이용해 RD 모델을 구현하는 것으로 많은 비트율 제어 방식 중 프레임을 기반으로 한 비트율 제어만을 본 논문에서는 고려한다. 특히 이 알고리즘은 오프라인에서 미리 트레이닝하는 것이 필요가 없기 때문에 실시간 코딩에도 적용 가능하다. VM18.0과의 비교 실험 결과들을 보면 본 논문에서 제안하는 비트율제어 알고리즘이 VMl8.0〔16〕에 비해 주관적인 화질 향상뿐만 아니라 적은 프레임 스킵(franc skip)과 높은 PSNR을 나타낸다.

Keywords

References

  1. B. Tao, B. W. Dickinson, H. A. Peterson, 'Adaptive model-driven bit allocation forMPEG video coding', IEEE Trans. Circuits Syst. Video Technol., Vol. 10, No. 1, pp. 147-157, 2000 https://doi.org/10.1109/76.825868
  2. G. H. Park, Y. J. Lee, 'Rate control algorithm using SOFM-based neural classifier', Electronics letters, Vol. 36, No. 12, pp. 1041-1042, 2000 https://doi.org/10.1049/el:20000777
  3. J. R. Corbera, S. M. Lei, 'A frame-layer bit allocation for H.263+', IEEE Trans. Circuits Syst. Video Technol., Vol. 10, No. 7, pp. 1154-1158, 2000 https://doi.org/10.1109/76.875518
  4. J. R. Corbera, S. M. Lei, 'Rate control in DCT video coding for low-delay communications', IEEE Trans. Circuits Syst. Video Technol., Vol. 9, No. 1, pp. 172-185, 1999 https://doi.org/10.1109/76.744284
  5. J. Nie, 'Constructing fuzzy model by selforganizing counterpropagation network', IEEE trans. Syst. Man Cyber., Vol. 25, No. 6, pp. 963-970, 1995 https://doi.org/10.1109/21.384258
  6. L. J. Lin, A. Ortega, 'Bit-rate control using piecewise approximated rate-distortion characteristics', IEEE Trans. Circuits Syst. Video Technol., Vol. 8, No. 4, pp. 446-549, 1998 https://doi.org/10.1109/76.709411
  7. M. Smith, 'Neural networks for statistical modeling', Van Nostrand Reinhold, New York, 1993
  8. T. Kohonen et al., 'Engineering applications of the self-organizing map', Proc. of IEEE, Vol. 84, No. 10, pp. 1358-1383, 1996 https://doi.org/10.1109/5.537105
  9. T. Chiang, Y. Q. Zhang, 'A new rate control scheme using quadratic rate distortion model', IEEE Trans. Circuits Syst. Video Technol., Vol. 7, No. 1, pp. 246-250, 1997 https://doi.org/10.1109/76.554439
  10. W. Ding, B. Liu, 'Rate control of MPEG video coding and recording by rate-quantization modeling', IEEE Trans. Circuits Syst. Video Technol., Vol. 6, No. 1, pp. 12-19, 1996 https://doi.org/10.1109/76.486416
  11. W. P. Li, J. R. Ohm et al, 'MPEG-4 Video Verification Model version 18.0', ISO/IEC JTC1/SC29/WG11/N3908, 2001
  12. Z. M. Zhang et al., 'An improved selforganizing CPN-based fuzzy system with adaptive back propagation algorithm', Fuzzy sets and systems, Vol. 130, No. 2, pp. 227-236, 2002 https://doi.org/10.1016/S0165-0114(01)00170-1