Accelerating Statistical Texture Analysis with an FPGA-DSP Hybrid Architecture

Nowadays, most image processing systems are implemented using either MMX-optimized software libraries or, when time requirements are limited, expensive high performance DSP-based boards. In this paper we present a texture analysis co-processor concept that permits the efficient hardware implementation of statistical feature extraction, and hardware-software codesign to achieve high-performance low-cost solutions. We propose a hybrid architecture based on FPGA chips, for massive data processing, and digital signal processor (DSP) for floating-point computations. In our preliminary trials with test images, we achieved sufficient performance improvements to handle a wide range of real-time applications.