Reinstating Floyd-Steinberg: Improved Metrics for Quality Assessment of Error Diffusion Algorithms
In this contribution we introduce a little-known property of error diffusion halftoning algorithms which we call error diffusion displacement. By accounting for the inherent sub-pixel displacement caused by the error propagation, we correct an important flaw in most metrics used to assess the quality of resulting halftones. We find these metrics to usually highly underestimate the quality of error diffusion in comparison to more modern algorithms such as direct binary search. Using empirical observation, we give a method for creating computationally efficient, image-independent, model-based metrics for this quality assessment. Finally, we use the properties of error diffusion displacement to justify Floyd and Steinberg's well-known choice of algorithm coefficients.
improved metrics,empirical observation,inherent sub-pixel displacement,error diffusion,error propagation,important flaw,quality assessment,model-based metrics,error diffusion algorithms,reinstating floyd-steinberg,algorithm coefficient,error diffusion displacement,direct binary search,human visual system,image quality,color quantization
Computer vision,Propagation of uncertainty,Computer science,Human visual system model,Error diffusion,Algorithm,Image quality,Artificial intelligence,Floyd–Steinberg dithering,Binary search algorithm,Color quantization