Though a color aperiodic, clustered-dot, halftoning (NPACMS-MP-CLU-DBS) algorithm can overcome the visible moire and rosette artifacts in conventional color halftoning methods, it still has some disadvantages, such as the color mismatch caused by the initial stage color management method, and texture artifacts caused by the concentric-ring cluster structure. In this paper, first, a new color gamut mapping method is used during the color management process, that is an image-dependent mapping method, which can make the most use of the printer color gamut, in order to reduce the color mismatch between the continuous-tone original and printed halftone images. Secondly, a new color, clustered-DBS halftoning algorithm with separated-cluster structure is developed. As a color halftoning method based on the clustered-DBS algorithm, not only it can overcome the visible moire and rosette artifacts, but also the separated-cluster structure is more stable, compared with the concentric-ring cluster structure. It can also reduce the texture artifacts significantly.
Camera denoising and sharpening parameters are device related rigid parameters which are programmed in phone camera device. The current tuning method depends solely on manual modulation and visual evaluation of image quality, which is time consuming and difficult to optimally achieve. To this end, we will introduce an automatic tuning method for mobile cameras in this paper, which can tune the WNR parameters automatically and produce high quality images within a feasible processing time. The method contains two parts, a perception model and an optimization algorithm. For the first part, we developed a perception model to evaluate the image quality for mobile cameras through modified CPIQ metrics. For the second part, in order to overcome a high-dimension non-convex optimization problem, we developed a searching strategy to find the optimal solution by conducting quantization and iteratively minimizing the error metric of the perception model.