We present an enhanced error diffusion halftoning algorithm for which the filter weights and the quantizer thresholds vary depending on input pixel value. The weights and thresholds are optimized based on a human visual system model. Based on an analysis of the edge behavior, a tone dependent threshold is designed to reduce edge effects and start-up delay. We also propose an error diffusion system with parallel scan that uses variable weight locations to reduce worms.

We present a clustered-minority-pixel error-diffusion halftoning algorithm for which the quantizer threshold is modified on the basis of the past output and a dot activation map. Dot area, dot shape, and dot distribution are more controllable than with other clustered-dot halftone algorithms such as Levien's algorithm. This method also effectively reduces structured mazelike artifacts in midtones that occur in Levien's algorithm. The dot distribution is further improved by using different error-diffusion weights for different input gray levels.

Error diffusion is a popular halftoning algorithm that in its most widely used form, is inherently serial. As a serial algorithm, error diffusion offers limited opportunity for large-scale parallelism. In some implementations, it may also result in excessive bus traffic between the on-chip processor and the off-chip memory used to store the modified continuous-tone image and the halftone image. We introduce a new error diffusion algorithm in which the image is processed in two groups of interlaced blocks. Within each group, the blocks may be processed entirely independently. In the first group, the error diffusion proceeds along an outward spiral from the center of the block. Errors along the boundaries of blocks in the first group are diffused into neighboring blocks in the second group, within which the error diffusion spirals inward. A tone-dependent error diffusion training framework is used to eliminate artifacts associated with the spiral scan paths. We demonstrate image quality that is close to that achieved by conventional line-by-line error diffusion.

Screening is a low complexity halftoning algorithm that has been widely used in many applications. However, screen design requires that the stacking property be obeyed. This constraint limits the texture quality at each gray level. We present a look-up-table based halftoning algorithm for which the stacking constraint is not necessarily satisfied; but the binary patterns for individual levels are still correlated. The binary patterns are designed level by level using the direct binary search method. The algorithm improves halftone image quality compared with screening.

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