REGION-ADAPTIVE TEXTURE-AWARE IMAGE RESIZING

In this paper, we analyze the effect of texture regularity on the performance of image resizing (or called image retargeting), and then propose an efficient texture-aware resizing algorithm. Being perceived as unimportant due to spatial homogeneity, textured patterns are largely deformed (or warped) in existing image resizing algorithms. However, arbitrary warping without considering the specific texture property tends to lead to noticeable visual artifacts. To address this issue, we exploit region features, including the scale and the shape information, to preserve both local and global structures. Guided by region and contour information, we propose a mesh-based resizing technique, which is formulated as a nonlinear least squares optimization problem and solved by an iterative Gauss-Newton method. Texture redundancy is effectively reduced through texture regularity analysis and real-time texture synthesis. The superior performance of the proposed image resizing technique is demonstrated by experimental results.