• DocumentCode
    3004472
  • Title

    Adaptive image and video retargeting technique based on Fourier analysis

  • Author

    Jun-Seong Kim ; Jin-Hwan Kim ; Chang-Su Kim

  • Author_Institution
    Sch. of Electr. Eng., Korea Univ., Seoul, South Korea
  • fYear
    2009
  • fDate
    20-25 June 2009
  • Firstpage
    1730
  • Lastpage
    1737
  • Abstract
    An adaptive image and video retargeting algorithm based on Fourier analysis is proposed in this work. We first divide an input image into several strips using the gradient information so that each strip consists of textures of similar complexities. Then, we scale each strip adaptively according to its importance measure. More specifically, the distortions, generated by the scaling procedure, are formulated in the frequency domain using the Fourier transform. Then, the objective is to determine the sizes of scaled strips to minimize the sum of distortions, subject to the constraint that the sum of their sizes should equal the size of the target output image. We solve this constrained optimization problem using the Lagrangian multiplier technique. Moreover, we extend the approach to the retargeting of video sequences. Simulation results demonstrate that the proposed algorithm provides reliable retargeting performance efficiently.
  • Keywords
    Fourier transforms; gradient methods; image sequences; image texture; video signal processing; Fourier analysis; Fourier transform; Lagrangian multiplier; adaptive image retargeting; constrained optimization; frequency domain; gradient information; image distortion; image texture; scaled strips; scaling procedure; video retargeting; video sequence; Algorithm design and analysis; Constraint optimization; Fourier transforms; Image analysis; Image segmentation; Lagrangian functions; Partitioning algorithms; Strips; TV; Video sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
  • Conference_Location
    Miami, FL
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4244-3992-8
  • Type

    conf

  • DOI
    10.1109/CVPR.2009.5206666
  • Filename
    5206666