Quality evaluation metric for greyscale error diffusion halftone images based on texture and visual characteristics

Abstract

ABSTRACTHalftone images produced by error diffusion algorithms suffer from texture structures that decrease their visual qualities, such as worm-like and stripe-like defects. An effective quality evaluation metric is needed to evaluate various error diffusion halftoning algorithms and their corresponding halftone images. A texture distortion evaluation metric (TDEM) is proposed to measure the texture distortions within the referenced halftoned greyscale images. First, the weights of different tonal regions were assigned based on the two types of visual objects available and the locations of the texture structures. Then, based on image structure correlations and human visual characteristics, a block operation was performed and TDEM values were obtained by summing the local block-weighted variances based on image information. The proposed metric was validated using objective comparison and psycho-visual experiments. Results indicate that the metric can measure texture distortions effectively, and that the results are consistent with subjective visual perception.