Image vectorization using blue-noise sampling

Current image vectorization techniques mainly deal with images with simple and plain colors. For full-color photographs, many difficulties still exist in object segmentation, feature line extraction, and color distribution reconstruction, etc. In this paper, we propose a high-efficiency image vectorization method based on importance sampling and triangulation. A set of blue-noise sampling points is first generated on the image plane by an improved error-diffusion sampling method. The point set well preserves the features in the image. Then after triangulation on this point set, color information can be recorded on the mesh vertices to form a vector image. After certain image editing, e.g. scaling or transforming, the whole image can be reconstructed by color interpolating inside each triangle. Experiments show that the method has high performing efficiency and abilities in feature-preserving. It will bring benefits to many applications, e.g. image compressing, editing, transmitting and resolution enhancement.