IEEE Trans Image Process. 2015 Sep;24(9):2811-26. doi: 10.1109/TIP.2015.2431441.
The popularity of stereo images and various display devices poses the need of stereo image retargeting techniques. Existing warping-based retargeting methods can well preserve the shape of salient objects in a retargeted stereo image pair. Nevertheless, these methods often incur depth distortion, since they attempt to preserve depth by maintaining the disparity of a set of sparse correspondences, rather than directly controlling the warping. In this paper, by considering how to directly control the warping functions, we propose a warping-based stereo image retargeting approach that can simultaneously preserve the shape of salient objects and the depth of 3D scenes. We first characterize the depth distortion in terms of warping functions to investigate the impact of a warping function on depth distortion. Based on the depth distortion model, we then exploit binocular visual characteristics of stereo images to derive region-based depth-preserving constraints which directly control the warping functions so as to faithfully preserve the depth of 3D scenes. Third, with the region-based depth-preserving constraints, we present a novel warping-based stereo image retargeting framework. Since the depth-preserving constraints are derived regardless of shape preservation, we relax the depth-preserving constraints to fulfill a tradeoff between shape preservation and depth preservation. Finally, we propose a quad-based implementation of the proposed framework. The results demonstrate the efficacy of our method in both depth and shape preservation for stereo image retargeting.
立体图像和各种显示设备的普及提出了立体图像重定向技术的需求。现有的基于变形的重定向方法可以很好地保持重定向立体图像对中显著对象的形状。然而,这些方法通常会产生深度失真,因为它们试图通过保持一组稀疏对应物的视差来保持深度,而不是直接控制变形。在本文中,通过考虑如何直接控制变形函数,我们提出了一种基于变形的立体图像重定向方法,该方法可以同时保持显著对象的形状和 3D 场景的深度。我们首先根据变形函数来描述深度失真,以研究变形函数对深度失真的影响。基于深度失真模型,我们利用立体图像的双目视觉特征推导出基于区域的深度保持约束,该约束直接控制变形函数,以真实地保持 3D 场景的深度。第三,基于基于区域的深度保持约束,我们提出了一种新颖的基于变形的立体图像重定向框架。由于深度保持约束是独立于形状保持推导出来的,我们放宽了深度保持约束,以在形状保持和深度保持之间实现权衡。最后,我们提出了一种基于四边形的实现方案。实验结果证明了我们的方法在立体图像重定向中的深度和形状保持方面的有效性。