Masking in visual recognition: effects of two-dimensional filtered noise

Abstract

It is difficult to recognize portraits that have been coarsely sampled and quantized. Blurring such images improves recognition. A simple, straightforward explanation is that high-frequency noise introduced by the sampling and quantizing must be removed by low-pass filtering to improve the signal-to-noise ratio and hence signal detectability or recognition. Experiments reported here, suggested on the basis of a different model, show instead that noise bands that are spectrally adjacent to the picture's spectrum are considerably more effective in suppressing recognition.