• DocumentCode
    1741648
  • Title

    Efficient prediction in multiple description video coding

  • Author

    Regunathan, Shankar L. ; Rose, Kenneth

  • Author_Institution
    Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    1020
  • Abstract
    This work is concerned with the design of multiple description (MD) compression systems with emphasis on video coding. Although inter-frame prediction is critical to the performance of video coders, the problem of efficient prediction in MD systems has not been satisfactorily resolved. We focus on quantizer based MD coding and propose an estimation theoretic (ET) approach to prediction and reconstruction. The advantage of ET prediction is two fold: (1) It takes into account all the information available at each decoder for an optimal estimate; (2) It mitigates the degradation due to quantization in the prediction feedback loop. The ET approach is first shown to achieve significant gains in the simpler setting of predictive MD coding of synthetic Gauss-Markov and Laplace-Markov processes. We then present performance results for MD compression of video sequences and demonstrate consistent and substantial PSNR gains over conventional techniques
  • Keywords
    Markov processes; estimation theory; image sequences; prediction theory; video coding; Gauss-Markov processes; Laplace-Markov processes; PSNR gain; efficient prediction; estimation theoretic prediction; estimation theory; inter-frame prediction; multiple description compression systems; prediction feedback loop; predictive coding; quantizer-based coding; video coding; video sequences; Decoding; Degradation; Estimation theory; Feedback loop; Gaussian processes; PSNR; Quantization; Video coding; Video compression; Video sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2000. Proceedings. 2000 International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1522-4880
  • Print_ISBN
    0-7803-6297-7
  • Type

    conf

  • DOI
    10.1109/ICIP.2000.901135
  • Filename
    901135