Human visual system (HVS) modeling has become a critical component in the design of digital halftoning algorithms. Methods that exploit the characteristics of the HVS include the direct binary search (DBS) and optimized tone-dependent halftoning approaches. The spatial sensitivity of the HVS is lowpass in nature, reflecting the physiological characteristics of the eye. Several HVS models have been proposed in the literature, among them, the broadly used Nasanen's exponential model. As shown experimentally by Kim and Allebach,1 Nasanen's model is constrained in shape and richer models are needed in order to attain better halftone attributes and to
control the appearance of undesired patterns. As an alternative, they proposed a class of HVS models based on mixtures of bivariate Gaussian density functions. The mathematical characteristics of the HVS model thus play a key role in the synthesis of model-based halftoning. In this work, alpha stable functions, an elegant class of
models richer than mixed Gaussians, are exploited. These are more efficient than Gaussian mixtures as they use less parameters to characterize the tails and bandwidth of the model. It is shown that a decrease in the model's bandwidth leads to homogeneous halftone patterns and conversely, models with heavier tails yield smoother
textures. These characteristics, added to their simplicity, make alpha stable models a powerful tool for HVS characterization.