Optimal bi-directional seam carving for compressibility-aware image retargeting

Abstract A novel compressibility-aware image retargeting method based on seam carving is introduced in this paper. We propose a new significance detection method, with both the edge information and visual saliency taken into consideration. A wall-seam model is constructed to evaluate the image compressibility and assign the right number of seams for each direction. By repeatedly carving out or inserting seams we can retarget the image to a new size while preserving the important content. Finally, our algorithm is completed with the supplement of uniformly scaling, the stretched image is resized to the target size with the least structure distortion brought. Experimental results on images show that those improvements are effective and our approach can preserve image content better compared to several state-of-the-art image retargeting methods.