近邻取样插值原理:
缩放后图片 原来的图片
对于缩放后图片中的某点 (Dx, Dy) 对应于原图片中的点 (Sx, Sy),它们之间存在如下的比例关系:
(Sx-0)/(SW-0)=(Dx-0)/(DW-0)
(Sy-0)/(SH-0)=(Dy-0)/(DH-0)
因此,已知缩放后图片中的任意一点 (Dx, Dy),可以求得其对应的原图片中的点 Sx=Dx*SW/DW,Sy=Dy*SH/DH。
近邻取样插值算法及其优化:
struct image_info {
int width; /* 图像宽度 */
int height; /* 图像高度 */
int bpp; /* 每像素占据的位数,单位bit */
int line_width; /* 图像每行占据的字节数,单位byte */
unsigned char *data; /* 图像像素数据 */
};
void zoom0(struct image *src_image, struct image *dst_image)
{
int x, y;
int srcx, srcy;
for(x=0; xwidth; x++)
{
for(y=0; yheight; y++)
{
srcx = (x * src_image->width / dst_image->width);
srcy = (y * src_image->height / dst_image->height);
Pixel(dst_image, x, y) = Pixel(src_image, srcx, srcy);
}
}
}
函数 zoom0() 并没有按照颜色数据在内存中的排列顺序读写(图片的像素数据是逐行存储的),将造成 CPU 缓存预读失败和内存颠簸导致巨大的性能损失,很多硬件都有这种特性,包括缓存、内存、显存、硬盘等,优化顺序访问,随机访问时会造成巨大的性能损失,所以先交换x,y循环的顺序:
void zoom1(struct image *src_image, struct image *dst_image)
{
int x, y;
int srcx, srcy;
for(y=0; yheight; y++)
{
for(x=0; xwidth; x++)
{
srcx = (x * src_image->width / dst_image->width);
srcy = (y * src_image->height / dst_image->height);
Pixel(dst_image, x, y) = Pixel(src_image, srcx, srcy);
}
}
}
可以发现 srcy 的值和 x 变量无关,可以提前到 x 循环之前:
void zoom2(struct image *src_image, struct image *dst_image)
{
int x, y;
int srcx, srcy;
for(y=0; yheight; y++)
{
srcy = (y * src_image->height / dst_image->height);
for(x=0; xwidth; x++)
{
srcx = (x * src_image->width / dst_image->width);
Pixel(dst_image, x, y) = Pixel(src_image, srcx, srcy);
}
}
}
每一行的缩放比例是固定的,那么可以预先建立一个缩放映射表格:
void zoom3(struct image *src_image, struct image *dst_image)
{
int x, y;
int srcy;
unsigned int *xtable;
xtable = (unsigned int *)malloc(dst_image->width * 4);
for(x=0; xwidth; x++)
xtable[x] = (x * src_image->width / dst_image->width);
for(y=0; yheight; y++)
{
srcy = (y * src_image->height / dst_image->height);
for(x=0; xwidth; x++)
{
Pixel(dst_image, x, y) = Pixel(src_image, xtable[x], srcy);
}
}
}
除法运算属于很慢的操作,比一般的加减运算慢几十倍,因此使用定点数的方法来优化它:
void zoom4(struct image *src_image, struct image *dst_image)
{
int x, y;
int srcy;
int xscale, yscale;
unsigned int *xtable;
xtable = (unsigned int *)malloc(dst_image->width * 4);
/* 函数能够处理的最大图片尺寸 65536*65536 */
xscale = (src_image->width << 16) / dst_image->width;
yscale = (src_image->height << 16) / dst_image->height
for(x=0; xwidth; x++)
xtable[x] = (x * xscale) >> 16;
for(y=0; yheight; y++)
{
srcy = (y * yscale) >> 16;
for(x=0; xwidth; x++)
{
Pixel(dst_image, x, y) = Pixel(src_image, xtable[x], srcy);
}
}
}
——Be loyal to dream. Be brave to try! linux_xpj@opencores.org
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