A novel print prediction model using ink‐jet printing characterisation

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Abstract A model for directly predicting the actual printed image produced by an ink‐jet printer is proposed. The distribution of ink dots in the printed image is determined solely by the halftone image, while the greyscale values of the sampled pixels are influenced by the blending of multiple ink colours, creating a complex mapping relationship. This paper presents a method to accurately measure ink dot displacement by establishing a deviation transformation relationship for each step of the ink‐jet‐to‐measurement process, enabling precise positioning of the nozzle offset. By combining the ink dot displacement data with the halftone image, an actual ink dot distribution model is constructed. A neural network is employed to fit the mapping between the ink dot distribution of the halftone image and the pixel greyscale values of the printed sample, thereby achieving the prediction of the real printed image. The experimental results demonstrate that the model can accurately predict the post‐press image and reflect the printer defects.

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