A Distortion-Sensitive Seam Carving Algorithm for Content-Aware Image Resizing

As displays become less expensive and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed (Avidan and Shamir, ACM Trans Graphics 26(3), 2007; Guo et al., IEEE Trans Multimedia 11(5):856–867, 2009; Shamir and Avidan, Commun ACM 52(1), 2009; Simakov et al. 2008; Wolf et al. 2007), which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose a distortion-sensitive seam carving algorithm for content-aware image resizing that improves edge preservation and decreases aliasing artifacts. In the proposed approach, we use local gradient information along with a thresholding technique to guide the seam selection process and provide a mechanism to halt seam carving when further processing would introduce unacceptable visual distortion in the resized image. Furthermore, anti-aliasing filter is used to reduce the aliasing artifacts caused by seam removal. Experiments have demonstrated superior performance over the current seam carving methods.