• DocumentCode
    2726109
  • Title

    Block-Mode Classification Using SVMs for Early Termination of Block Mode Decision in H.264|MPEG-4 Part 10 AVC

  • Author

    Kim, Jaeil ; Kim, Munchurl ; Hahm, Sangjin ; Cho, In-joon ; Park, Changsub

  • Author_Institution
    Inf. & Commun. Univ., Daejeon
  • fYear
    2009
  • fDate
    4-6 Feb. 2009
  • Firstpage
    83
  • Lastpage
    86
  • Abstract
    In this paper, a two-stage block-mode classification scheme of H.264|MPEG-4 Part 10 AVC is presented as a pattern classification approach using SVMs in order to reduce high computational complexity of its encoders. For the block-mode classification, the feature vectors for each macroblock are formed for the SVMs with SATD and CBP values to detect the large and small block modes. From the experimental results, the proposed scheme yields 80% and 95% of the correct classification rate in average for the first and second stage, which has led to from 35% to 55% reduction in the total encoding time while maintaining negligible amounts of bit rate increases and PSNR drops for test sequences with QCIF, CIF, and 4CIF resolutions and various quantization parameter values.
  • Keywords
    block codes; computational complexity; feature extraction; image classification; support vector machines; video coding; H.264-MPEG-4 Part 10 AVC; SVM; computational complexity; early block mode decision termination; encoder; feature vector; pattern classification; sum-of-absolute transformed difference value; support vector machine; two-stage block-mode classification; Automatic voltage control; Broadcasting; Computational complexity; Costs; Electronic mail; Encoding; Pattern recognition; Predictive coding; Quadratic programming; Quantization; H.264|MPEG-4 Part 10 AVC; fast mode decision; inter/intra mode selection; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on
  • Conference_Location
    Kolkata
  • Print_ISBN
    978-1-4244-3335-3
  • Type

    conf

  • DOI
    10.1109/ICAPR.2009.65
  • Filename
    4782747