VLSI architectures for weighted order statistic (WOS) filters

Abstract

The class of median filters has been extended to include weighted order statistics (WOS) filters, to improve the flexibility of the filtering operation. The WOS filter weights each input within a sample window, and thus retains the original temporal order information. In this paper, we present efficient VLSI architectures for WOS filters which maintain a weighted rank for each sample in the sample window and update the weighted ranks for each window shift. We present novel (i) array architectures, (ii) stack filter architectures and (iii) sorting network architectures for non-recursive and recursive WOS filters which implement the above procedure. Our analysis shows that the bit-serial stack filter implementation is the one with the smallest area while the bit-parallel stack filter is the one with the smallest input–output latency. The sorting network architecture (based on updating a sorted list) has the best area–time performance. Physical implementations verify our analysis.