In previous work  , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth
areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented.
Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral
filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.