Optimized scale-and-stretch for image resizing

Yu-Shuen Wang*, Chiew Lan Tai, Olga Sorkine, Tong Yee Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

445 Scopus citations


We present a "scale-and-stretch" warping method that allows resizing images into arbitrary aspect ratios while preserving visually prominent features. The method operates by iteratively computing optimal local scaling factors for each local region and updating a warped image that matches these scaling factors as closely as possible. The amount of deformation of the image content is guided by a significance map that characterizes the visual attractiveness of each pixel; this significance map is computed automatically using a novel combination of gradient and salience-based measures. Our technique allows diverting the distortion due to resizing to image regions with homogeneous content, such that the impact on perceptually important features is minimized. Unlike previous approaches, our method distributes the distortion in all spatial directions, even when the resizing operation is only applied horizontally or vertically, thus fully utilizing the available homogeneous regions to absorb the distortion. We develop an efficient formulation for the nonlinear optimization involved in the warping function computation, allowing interactive image resizing.

Original languageEnglish
Article number118
Pages (from-to)1-8
JournalACM Transactions on Graphics
Issue number5
StatePublished - 1 Dec 2008


  • Arbitrary image resizing
  • Nonlinear optimization
  • Visual saliency


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