最优化网格形变的图像非均匀映射方法
Content-Aware Image Retargeting Using Optimization Grid Deformation
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摘要: 本文针对内容感知的图像非均匀映射问题,建立了一种有效的基于网格形变的全局最优化模型,引入了一系列保护重要网格形变的能量约束项,使得当图像尺寸比例发生改变时,视觉显著的重要区域不发生形变,并且尽可能地保留图像中的全局信息,给予用户最佳的图像浏览体验。此外,也通过建立网格初始划分问题的最优化数学模型,实现覆盖重要区域的网格密集分布,减少关键对象边界信息的扭曲失真。通过与其他主流方法的结果比较与分析,证明本文方法能够更好的保证图像关键区域不发生形变,得到质量更优的结果图像。Abstract: We built an effective global optimization model of image retargeting based on grid deformation。This model introduces a series of energy functions that prevent important grid deforming 。The proposed approach allows resizing images into arbitrary aspect ratio while protecting visually prominent area and retaining the global information as much as possible,in order to give users the best experience of browsing image。In addition,we achieved that goal that important grid distribute densely by establishing the optimization model of partitioning the initial grid in the original image,reducing the artifacts on the boundary of the prominent object 。A number of comparison experiments show that our method performs better than state-of-the-art approaches。