Estimation of Motion Sickness in Automated Vehicles using Stereoscopic Visual Simulation

Abstract

Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixel’s neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures.