Image and Video Retargetting by Darting

  • Authors:
  • Matthew Brand

  • Affiliations:
  • Mitsubishi Electric Research Labs,

  • Venue:
  • ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
  • Year:
  • 2009

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Abstract

This paper considers the problem of altering an image by imperceptibly adding or removing pixels, for example, to fit a differently shaped frame with minimal loss of interesting content. We show how to construct a family of convex programs that suitably rearrange pixels while minimizing image artifacts and distortions. We call this "darting" on analogy to a tailor's darts--small edits are discreetly distributed throughout the fabric of the image. We develop a reduction to integer dynamic programming on edit trellises, yielding fast algorithms. One- and two-pass variants of the method have O (1) per-pixel complexity. Of the many edits that darting supports, five are demonstrated here: image retargetting to smaller aspect ratios; adding or moving or removing scene objects while preserving image dimensions; image expansion with gaps filled by a rudimentary form of texture synthesis; temporal video summarization by "packing" motion in time; and an extension to spatial video retargetting that avoids motion artifacts by preserving optical flow.