Abstract. Visual salience detection originated over 500 million years ago and
is one of nature’s most efficient mechanisms. In contrast, many state-of-the-art
computational saliency models are complex and inefficient. Most saliency models
process high-resolution color images; however, insights into the evolutionary origins of visual salience detection suggest that achromatic low-resolution vision is
essential to its speed and efficiency. Previous studies showed that low-resolution
color and high-resolution grayscale images preserve saliency information. However, to our knowledge, no one has investigated whether saliency is preserved in
low-resolution grayscale (LG) images. In this study, we explain the biological
and computational motivation for LG, and show, through a range of human eyetracking and computational modeling experiments, that saliency information is
preserved in LG images. Moreover, we show that using LG images leads to significant speedups in model training and detection times and conclude by proposing LG images for fast and efficient salience detection