Block-level parallel processing for scaling evenly divisible images

Image scaling is a frequent operation in medical image processing. This paper presents how two-dimensional (2-D) image scaling can be accelerated with a new coarse-grained parallel processing method. The method is based on evenly divisible image sizes which is, in practice, the case with most medical images. In the proposed method, the image is divided into slices and all the slices are scaled in parallel. The complexity of the method is examined with two parallel architectures while considering memory consumption and data throughput. Several scaling functions can be handled with these generic architectures including linear, cubic B-spline, cubic, Lagrange, Gaussian, and sinc interpolations. Parallelism can be adjusted independent of the complexity of the computational units. The most promising architecture is implemented as a simulation model and the hardware resources as well as the performance are evaluated. All the significant resources are shown to be linearly proportional to the parallelization factor. With contemporary programmable logic, real-time scaling is achievable with large resolution 2-D images and a good quality interpolation. The proposed block-level scaling is also shown to increase software scaling performance over four times.