Multi-seam carving via seamlets

Seam carving is a powerful retargeting algorithm for mapping images to arbitrary sizes with arbitrary aspect ratios. Meanwhile, the seamlet transform has been introduced as an efficient image representation for seam-carving-based retargeting over heterogeneous multimedia devices with a broad range of display sizes. The original seamlet transform was developed using Haar filters, and hence it enabled traditional single-seam carving by removing a single seam at a time in a recursive manner until the desired image size was reached. In this paper, we develop a more efficient approach for seam carving by enabling multi-seam carving, where at each step of the retargeting algorithm, multiple seams are carved simultaneously. We achieve multi-seam carving by (a) extending the seamlet transform to allow for larger filters, and (b) employing local circular convolution in the vicinity of the selected seams. We show that by extending the seamlet transform we can employ popular filterbanks such as Daubechies' wavelets to achieve efficient multi-seam carving with visual quality that is comparable to single-seam carving using the Haar transform. Furthermore, with multi-seam carving, the number of iterations needed to achieve a given target size can be reduced significantly.