In order to eliminate the graphic aliasing more quickly and efficiently, we propose a sub-pixel level precision regional sampling anti-aliasing algorithm based on integer coordinate, putting sub-pixel information on the low bits and the integral parts on the high bits of plastic number. Calculating the cover area of each pixel with bit operation according to the difference between the sub-pixel part and the integer part. The Brightness level can reach 2~n(n is the length of sub-pixel, most of the time it is 8). Compared with mid-point based regional sample algorithm, the picture generated is more precise and fast. Compared with over sampling method( most of the time use 3×3 or 4×4 sample grid) , it' s more precise, when the sample bits arise, the calculation will be the same, and doesn' t exist information losing problems when the data is conversed from high resolution to low resolution witch results aliasing. The result shows our algorithm can quickly generate high quality images better than general antialiasing algorithm.%为了快速有效地消除图形显示中的边界锯齿,提出了一种基于整数坐标的亚像素精度区域采样反走样算法.该算法将坐标点的亚像素部分保存于整数的低位,整数部分存于高位,根据亚像素部分与整数部分的差值,利用位运算快速计算像素的覆盖面积,像素亮度等级可达到2~n(n为亚像素位数,通常情况n取8).与基于中点算法的区域采样算法相比,图像精度高,计算速度快;与过采样算法(采用3×3或4×4的过采样网格)相比,采样精度高,计算量不会随着采样精度的提高而变大,也不存在图像重建过程中由高分辨率数据向低分辨率转变时的信息丢失问题.实验结果表明,新算法生成的图像比较细腻,实时性比较高,优于一般的反走样算法.
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