Error diffusion is a halftoning method which employs feedback to preserve the local image intensity and reduce low frequency quantization noise. Error diffusion essentially sharpens the original and adds highpass quantization noise. As a consequence, PSNR is not an appropriate quality measure. As printer resolution increases, images must be enlarged and halftoned, so fast low-cost interpolated halftoning systems become important. However, halftones cannot be scaled, sharpened, rotated, or compressed without causing severe degradation.
Inverse halftoning essentially blurs the halftone and adds noise.
Rehalftoning, which is inverse halftoning followed by halftoning, is a technique under consideration for lossy coding of binary images for the emerging JBIG2 standard. The talk presents objective measures for the frequency distortion and noise component in forward and inverse halftoning. The measures are based on modeling the quantizer in error diffusion as a gain plus additive noise and modeling the human visual system as a lowpass linear system. We compare competing forward and inverse halftoning schemes in terms of computational complexity, PSNR, and quality. This talk also presents a new algorithm for inverse halftoning, which is based on anisotropic diffusion and is well-suited for implementation in VLSI circuits and embedded software.