Optimized scale-and-stretch for image resizing

Abstract

Figure 1: We partition the original image (left) into a grid mesh and deform it to fit the new desired dimensions (right), such that the quad faces covering important image regions are optimized to scale uniformly while regions with homogeneous content are allowed to be distorted. The scaling and stretching of the image content is guided by a significance map which combines the gradient and the saliency maps. AbstractWe 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.

Content Aware Image Resizing with Constraint of Object Aspect Ratio Preservation

“…Resizing methods with combination of seam carving and depth information were proposed in [6], [7]. Warping methods [8]- [10] are other approaches for content-aware image resizing. They place a mesh onto an image and then deform the mesh by computing a new geometry for the mesh.…”

“…However, if the target image width is smaller than the width of important content on an original image, these methods fail to prevent important content from changing. Instead of enforcing the size of important content to remain unchanged, the optimized scale-and-stretch approach [8], which is categorized as a warping method, determines an optimal scaling rate for each local region on a mesh. Their method distributes the distortion to image regions with homogeneous content.…”

“…In the warping methods that allow vertices to move to any directions, the grid lines are likely to bend. This causes large distortion as described in [8]. To minimize the bending of the grid lines, Wang et al [8] tried to retain the edge orientations during mesh deformation.…”

“…This causes large distortion as described in [8]. To minimize the bending of the grid lines, Wang et al [8] tried to retain the edge orientations during mesh deformation. In the resizing results of [8], the edge lengths are changed while many edges are linearly aligned.…”

“…To minimize the bending of the grid lines, Wang et al [8] tried to retain the edge orientations during mesh deformation. In the resizing results of [8], the edge lengths are changed while many edges are linearly aligned. From this observation, we can see the advantage of our mesh transformation, which changes the distances between the grid lines.…”