A binary noisy channel to model errors in printing process

TL;DR

This paper introduces a binary noisy channel model to analyze errors in printing processes, using information theory to evaluate the robustness of different halftoning algorithms against printing noise.

Contribution

It presents a novel binary noisy channel model for printing errors and applies relative entropy to assess halftoning algorithm robustness to noise.

Findings

Relative entropy effectively measures algorithm immunity to printing noise

Different halftoning algorithms show varying robustness levels

The model enables comparison of noise resilience in printing processes

Abstract

To model printing noise a binary noisy channel and a set of controlled gates are introduced. The channel input is an image created by a halftoning algorithm and its output is the printed picture. Using this channel robustness to noise between halftoning algorithms can be studied. We introduced relative entropy to describe immunity of the algorithm to noise and tested several halftoning algorithms.