A halftone image quality assessment method based on gradient and texture

Digital halftoning is an important screening technique in the digital printing and publishing. However, traditional image quality assessment (IQA) methods are not fully applicable to the quality assessment of halftone images. This paper proposes a gradient and texture-based quality assessment method for halftone images, PGT-SSIM (Partitioned Gradient and Texture Structural Similarity). The proposed method builds on the partitioning concept, extracts the gradient feature map of the image, incorporates texture feature differences, and finally applies the SSIM formula for weighted scoring to derive the final quality score. Experimental results demonstrate that the proposed method achieves higher accuracy and better alignment with human subjective perception compared to existing approaches. The indexes of PGT-SSIM are significantly higher than SSIM. The PLCC index of PGT-SSIM is 11% higher than that of Partition SSIM. Furthermore, the proposed halftone IQA method provides valuable insights for improving halftone algorithms, making it a significant contribution to the field.