Subpixel-Based Image Down-Sampling With Min-Max Directional Error for Stripe Display

Abstract

Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. In this paper, we formulate the subpixel-based down-sampling as a Min-Max problem (Min-Max Directional Error) which we call MMDE. Unfortunately, the solution of MMDE is computational intensive, especially for large images. We thus relax the MMDE by approximating the maximization operation based on HVS, which we call MMDE with Visual Relaxation (MMDE-VR). Although MMDE-VR can reduce the computational complexity of MMDE largely, it is still not acceptable for real-time implementation. We make a further simplifying assumption based on the edge relationship of neighboring pixels, which we call MMDE with Edge Relaxation (MMDE-ER). Simulation results show that MMDE-VR and MMDE-ER can give significant sharper images compared with the conventional pixel-based down-sampling methods, with little color fringing artifacts.