In this paper we present a novel method to map high dynamic range scenes to low dynamic range images for visualization. We formulate the problem as a quantization process and employ an adaptive learning strategy to ensure that the low dynamic range displays not only faithfully reproduce the original scenes but also are visually pleasing. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism. An L₂ objective function ensures that the mapped low dynamic image preserves the relative visual contrast impressions of the original scene. A frequency sensitive competitive mechanism facilitates the full and even utilization of the limited displayable values. We present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes.