Abstract: Highly advantageous in efficiency, the algorithm of temporal anti-aliasing has been one of the most widely employed real-time anti-aliasing algorithms in recent years. To achieve real-time anti-aliasing, this algorithm assigns the sampling points to the multiple history frames and reuses historical data. When the sampling information in the time domain is sufficient and historical data is usable, it can achieve a similar effect as supersampling anti-aliasing can. However, problems such as geometric edge jagging, ghosting, and subpixel detail missing will arise in the use of this algorithm in practical applications. Based on the research of temporal anti-aliasing, this thesis proposed the future-frame-based temporal anti-aliasing to solve the problems. The basic idea of the proposed algorithm was that: using the existing information in the time domain, it took the next aliasing future frame into account, using samples in future frame to enhance the geometric anti-aliasing effect, achieve anti-ghosting, and improve the situation of subpixel detail missing. In the process of implementation, this thesis proposed solutions to reusable future data extraction and future data reuse. Experimental results indicate that compared with the temporal anti-aliasing, the proposed algorithm can yield a better effect. 

Key words:  temporal anti-aliasing, future frame, geometric anti-aliasing, anti-ghosting, subpixel detail missing