LAUSR

A low-complexity pruned eight-point discrete cosine transform (DCT) approximation for image compression in visual sensor networks is introduced. The proposed transform consists of using an approximate DCT in combination with pruning approach. The aim of the former is to reduce the computational complexity by not computing the DCT exactly, while the latter aims at computing only the more important low-frequency coefficients. An algorithm for the fast computation of the proposed transform is developed. Only ten additions are required for both forward and backward transformations. The proposed pruned DCT transform exhibits extremely low computational complexity while maintaining competitive image compression performance in comparison with the state-of-the-art methods. An efficient parallel-pipelined hardware architecture for the proposed pruned DCT is also designed. The resulting design is implemented on Xilinx Virtex-6 XC6VSX475T-2ff1156 FPGA technology and evaluated for hardware resource utilization, power consumption, and real-time performance. All the metrics we investigated showed clear advantages of the proposed pruned approximate transform over the state-of-the-art competitors.