ACCELERATED SEAM CARVING USING CUDA

Abstract

The purpose of this review paper is to show the difference between executing the seam carving algorithm using sequential approach on a traditional CPU (central processing unit) and using parallel approach on a modern CUDA (compute unified device architecture) enabled GPU (graphics processing unit). Seam Carving is a content-aware image resizing method proposed by Avidan and Shamir of MERL .[1] It functions by identifying seams, or paths of least importance, through an image. These seams can either be removed or inserted in order to change the size of the image. It is determined that the success of this algorithm depends on a lot of factors: the number of objects in the picture, the size of monotonous background and the energy function. The purpose of the algorithm is to reduce image distortion in applications where images cannot be displayed at their original size. CUDA is a parallel architecture for GPUs, developed in the year 2007 by the Nvidia Corporation. Besides their primary function i.e. rendering of graphics, GPUs can also be used for general purpose computing (GPGPU). CUDA enabled GPU helps its user to harness massive parallelism in regular computations. If an algorithm can be made parallel, the use of GPUs significantly improves the performance and reduces the load of the central processing units (CPUs). The implementation of seam carving uses massive matrix calculations which could be performed in parallel to achieve speed ups in the execution of the algorithm as a whole. The entire algorithm itself cannot be run in parallel, and so some part of the algorithm mandatorily needs a CPU for performing sequential computations.