An implementation framework for fast image processing

Efficient processing of image data is a key aspect of achieving real-time performance for image and video applications. Here, a biologically inspired novel framework which uses a spiral indexing scheme is used to facilitate fast image processing. In particular we demonstrate the effectiveness of our approach on low-level image operations (convolution) and feature extraction (edge detection). Unlike conventional image addressing schemes where the pixels are indexed using two-dimensional Cartesian coordinates, a spiral addressing scheme enables the pixels to be stored in memory adjacent to their immediate neighbours and indexed as a one-dimensional vector. This permits both efficient traversal of the image structure and efficient application of image processing operators. Performance is evaluated by the application of Laplacian edge detection. The results demonstrate the efficiency of the proposed approach compared with a typical two-dimensional implementation.