A novel spiral addressing scheme for rectangular images

Abstract

Spiral architectures have been employed as an efficient addressing scheme in hexagonal image processing (HIP), whereby the image pixel indices can be stored in a one-dimensional vector that enables fast image processing. However, this computational advance of HIP is hindered by the additional time and effort required for conversion of image data to a HIP environment, as existing hardware for image capture and display are based predominantly on traditional rectangular pixels. In this paper, we present a novel spiral image processing framework that develops an efficient spiral addressing scheme for standard square images. We refer to this new framework as “squiral” (square spiral) image processing (SIP). Unlike HIP, conversion to the SIP addressing scheme can be achieved easily using an existing lattice with a Cartesian coordinate system; there is also no need to design special hexagonal image processing operators. Furthermore, we have developed a SIP-based non-overlapping convolution technique by simulating the “eye tremor” phenomenon of the human visual system, which facilitates fast computation. For illustration we have implemented this technique for the purpose of edge detection. The preliminary results demonstrate the efficiency of the SIP framework by comparison with standard 2D convolution and separable 2D convolution.