Approximate Arithmetic for Low-Power Image Median Filtering

In applications related to human senses, such as audio and image processing, computations with limited precision are acceptable. In these areas, a digital system can be implemented using approximate computing that works with sufficient precision. In this paper, we present a method to design 2-bit approximate magnitude comparators that are effectively low cost in terms of power, area and speed. We build larger comparators with adjustable error characteristics. Compared to precise one, our approximate comparators can save power and area up to 7–46 % and 10–50 %, respectively. The structures of our comparators and their error characteristics are presented in this paper. We use these comparators to design different approximate image median filters in order to remove salt and pepper noise. Simulation results show that the output quality of these filters is very similar to that of the precise ones so that the degradation is not noticeable. The approximate filters save up to 30 % of power and area while working 18 % faster than the precise ones.