Optimized error diffusion based on a human visual model

Abstract

Error diffusion is a powerful means to improve the subjective quality of a quantized image by shaping the spectrum of the display error. Considering an image in raster ordering, this is done by adding a weighted sum of previous quantization errors to the current pixel before quantization. These weights form an error diffusion filter. In this paper a method is proposed to find an optimized error diffusion filter for image display applications. The design is based on the lowpass characteristic of the contrast sensitivity of the human visual system. The filter is chosen so that a cascade of the quantization system and the observer's visual modulation transfer function yields a whitened spectrum of error. It is shown in this paper that the optimal error diffusion filter corresponds to a linear prediction filter of the human visual transfer function. A first order linear filter for an underlying non-separable vision model is examined. The resulting images contain mostly high frequency components of the display error, which are less noticeable for the viewer. This corresponds well to previously published results about the visibility of halftoning patterns. An informal comparison with other error diffusion algorithms shows less artificial contouring and increased image quality.