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Image and Video Retargetting by Darting

  • Matthew Brand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5627)

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.

Keywords

Texture Synthesis Small Aspect Ratio Nonrigid Motion Rudimentary Form Replica Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matthew Brand
    • 1
  1. 1.Mitsubishi Electric Research LabsUSA

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