Optimizing Seam Carving on multi-GPU systems for real-time image resizing

Abstract

Image resizing is increasingly important for picture sharing and exchanging between various personal electronic equipments. Seam Carving is a state-of-the-art approach for effective image resizing because of its content-aware characteristic. However, complex computation and memory access patterns make it time-consuming and prevent its wide usage in real-time image processing. To address these problems, we propose a novel algorithm, called Non-Cumulative Seam Carving (NCSC), which removes main computation bottleneck. Furthermore, we also propose an adaptive multi-seam algorithm for better parallelism on GPU platforms. Finally, we implement our algorithm on a multi-GPU platform. Results show that our approach achieves a maximum 140× speedup on a two-GPU system over the sequential version. It only takes 0.11 second to resize a 1024×640 image by half in width compared to 15.5 seconds with the traditional seam carving.