可靠的视觉感知估计体现的是对图像中有意义的那部分结构信息的理解。基于此,描述一个完整的图像和视频抽象化框架,通过一种边缘保持的滤波器有区分地风格化抽象图像和视频的显著部分和非显著部分,并保持两部分之间的和谐过渡。首先,对于显著区域的识别引入一种自动显著性目标分割算法,基于局部空间邻域的自动显著性计算。考虑到实际需要,提供了交互式技术,方便有目的地去指定图像中的显著结构信息。然后,根据生成的显著结构信息掩模使用单尺度的各向异性滤波处理显著部分,而用多尺度的同种滤波处理非显著部分以实现更强程度的抽象效果。本文方法生成的图像和视频的抽象化不仅呈现了视觉满意的非真实感效果,而且在软量化后还可以应用于其他风格的非真实感渲染(NPR)效果的实现。实验结果显示,处理一定数量的图像及视频,本文方法可以得到期望的结果。

Reliable estimation of visual perception reflects the understanding of the meaningful structure in an image. In this paper, we describe a complete abstraction framework for images and videos that explicitly responds to this goal. The method stylizes salient images or video parts and non-salient images or video parts differently by an edge preserving filter, and keeps a harmonious transition between the two parts. First, we introduce an automatic salient object segmentation algorithm to distinguish salient regions, and it is a saliency computation based local spatial neighbors. Taking into account the actual needs, we provide an interactive technology, which can be convenient for specifying image salient structure information on purpose. Based on the generated salient structure information mask, we use a single-scale anisotropic filter to process the salient parts, and use a multi-scale anisotropic filter to process the non-salient parts so that we can implement a strong abstraction effect.The proposed method generates a kind of image and video abstraction that does not only represent a preferable visual effect, but also can be applied to the implementation of another non-photorealistic rendering (NPR)result after soft image quantization. Experiments show that our algorithm could get the desired result for processing a certain number of images and videos.