Halftone image quality analysis based on a human vision model

The perceived quality of a printed image depends on the halftone algorithm and the printing process. This paper proposes a new method of analyzing halftone image quality in the frequency domain based on a human vision model. First, the Fourier transform characteristics of a dithered image are reviewed. Several commonly used dither algorithms, including clustered-dot dither and dispersed-dot dither, are evaluated based on their Fourier transform characteristics. Next, images halftoned with the dither algorithms and the Floyd-Steinberg error diffusion algorithm are compared in the frequency domain. Factors affecting printed image quality in a printing process are also discussed. Finally, a perception-based halftone image distortion measure is proposed. This measure reflects the quality of a halftone image printed on an ideal bi-level device and viewed at a particular distance. The halftone algorithms are ranked according to the proposed distortion measure. The effects of using human visual models with different peak sensitivity frequencies are examined.