Fast volume seam carving with multi-pass dynamic programming

In volume seam carving, seam carving for three-dimensional (3D) cost volume, an optimal seam surface can be derived by graph cuts, resulting from sophisticated graph construction. However, the graph cuts algorithm is not suitable for practical use because it incurs a heavy computational load. We propose a multi-pass dynamic programming (DP) based approach for volume seam carving that reduces computation time to 60 times faster and memory consumption to 10 times smaller than those of graph cuts, while maintaining a similar image quality as that of graph cuts. In our multi-pass DP, a suboptimal seam surface is created instead of a globally optimal one, but it has been experimentally confirmed by more than 198 crowd workers that such suboptimal seams are good enough for image processing.

[1]  Pradeep Sen,et al.  Video Carving , 2008, Eurographics.

[2]  Jan Kautz,et al.  Local Laplacian filters: edge-aware image processing with a Laplacian pyramid , 2011, ACM Trans. Graph..

[3]  Ariel Shamir,et al.  Improved seam carving for video retargeting , 2008, SIGGRAPH 2008.

[4]  S. Avidan,et al.  Seam carving for content-aware image resizing , 2007, SIGGRAPH 2007.

[5]  Bo Yan,et al.  Matching-Area-Based Seam Carving for Video Retargeting , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Karol Myszkowski,et al.  Adaptive Logarithmic Mapping For Displaying High Contrast Scenes , 2003, Comput. Graph. Forum.

[7]  Jian-Jiun Ding,et al.  Coarse-to-fine temporal optimization for video retargeting based on seam carving , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[8]  Zhou Wang,et al.  Objective Quality Assessment of Tone-Mapped Images , 2013, IEEE Transactions on Image Processing.

[9]  Mei Han,et al.  Discontinuous seam-carving for video retargeting , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Bo Gu,et al.  Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping , 2013, IEEE Transactions on Image Processing.

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  Janusz Konrad,et al.  Video Condensation by Ribbon Carving , 2009, IEEE Transactions on Image Processing.