A printer model using signal processing techniques

An accurate printer model that is efficient enough to be used by halftoning algorithms is proposed. The proposed signal processing model (SPM) utilizes a physical model to train adaptive linear combiners (ALCs), after which the average exposure of each subpixel for any input pattern can be calculated using the optimized weight vector. The SPM can be used to model multi-level halftoning and resolution enhancement, as well as traditional halftoning. The SPM is comprised of a single ALC layer followed by a peak-to-average ratio (PAR) correction layer, which serves to produce a PAR of less than 1.5 in the modeled exposure. The PCN (PAR correction network) employs one ALC/pixel and exploits the physics governing the characteristics of exposure in small regions. A relatively small number of training patterns suffices to train the SPM.