Novel “Squiral” (square spiral) architecture for fast image processing

Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as “squiral”) architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as “Squiral” Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed “squiral” architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the efficiency of the proposed SIP framework compared with standard 2D convolution.