• DocumentCode
    248865
  • Title

    Mode-dependent distortion modeling for H.264/SVC coarse grain SNR scalability

  • Author

    Yin-An Jian ; Chun-Chi Chen ; Wen-Hsiao Peng

  • Author_Institution
    Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    3165
  • Lastpage
    3169
  • Abstract
    This paper presents a mode-dependent distortion model for H.264/SVC coarse grain SNR scalability. It estimates the base-layer and enhancement-layer´s distortions with particular consideration of their prediction modes and inter-layer residual prediction. Based on a parametric signal model, the variances of the transformed prediction residual at both layers are first formulated analytically and approximated empirically. The results are then incorporated into the assumption that the transform coefficients are distributed according to the Laplacian distribution to obtain the final distortion estimates. Experimental results confirm its fairly good ability to predict the actual distortions in both the frame and macroblock levels.
  • Keywords
    Laplace transforms; image enhancement; video coding; H.264-SVC coarse grain SNR scalability; Laplacian distribution; base-layer distortion; enhancement-layer distortion; final distortion estimates; frame level; interlayer residual prediction; macroblock level; mode-dependent distortion modeling; parametric signal model; prediction modes; transform coefficients; transformed prediction residual variances; Discrete cosine transforms; Distortion measurement; Encoding; Predictive models; Quantization (signal); Vectors; Scalable video coding; coarse grain SNR scalability; distortion modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2014 IEEE International Conference on
  • Conference_Location
    Paris
  • Type

    conf

  • DOI
    10.1109/ICIP.2014.7025640
  • Filename
    7025640