Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a s...Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware iraage resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast （e.g. our method needs only about 100 ms to reduce a 768x1024 Image＇s width to 1/3 while Avidan and Shamir＇s method needs 12 s）. This supports immediate image resizing whereas Avidan and Shamir＇s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.展开更多
基金Supported by National Natural Science Foundation of China (Grant Nos.60575002 and 60641002)
文摘Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware iraage resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast （e.g. our method needs only about 100 ms to reduce a 768x1024 Image＇s width to 1/3 while Avidan and Shamir＇s method needs 12 s）. This supports immediate image resizing whereas Avidan and Shamir＇s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.
