Image Processing for Very Large Images
The key idea here is the partial image descriptor
VIPS(VASARI Image Processing System) 是近几年逐渐兴起的针对大图像数据高效处理的开源库，下面给出一个表格显示出其相对于目前的一些其他开源库，针对同一组5000
| Software | Run time (secs real) | Memory (peak RSS MB) | Times slower |
|---|---|---|---|
| VIPS C/C++ 8.1 | 0.20 | 43 | 1.0 |
| Python VIPS 8.1 | 0.30 | 52 | 1.5 |
| VIPS command-line 8.1 | 0.55 | 40 | 2.4 |
| VIPS C/C++ 8.1, JPEG images | 0.38 | 59 | 2.7 |
| ymagine 0.7.0 | 1.07 | 2.7 | 2.8 (compared to vips-c JPEG) |
| GraphicsMagick 1.3.20 | 0.67 | 492 | 3.4 |
| sips 10.4.4 | 0.74 (est.) | 268 | 3.7 |
| ImageMagick 6.8.9-9 | 0.78 | 484 | 3.9 |
| VIPS nip2 8.1 | 0.79 | 78 | 4.0 |
| RMagick 2.15.2 (ImageMagick 6.8.9) | 0.87 | 684 | 4.4 |
| NetPBM 10.0 | 0.93 | 76 | 4.7 |
| Pillow 2.7.0 | 0.93 | 207 | 4.7 |
| OpenCV 2.4.9 | 1.13 | 206 | 5.7 |
| libgd 2.1.1 | 2.34 | 186 | 6.1 (compared to vips-c JPEG) |
| Imlib2 1.4.7 | 1.53 | 250 | 7.7 |
| ExactImage 0.8.9 | 1.54 | 130 | 7.7 |
| FreeImage 3.15.4 (incomplete) | 1.63 | 183 | 8.1 |
| gmic 1.5.7.1 | 1.87 | 700 | 9.35 |
| ImageScience 1.2.6 (based on FreeImage 3.15.4, incomplete) | 1.9 | 267 | 9.5 |
| OpenImageIO 1.3.12 | 2.79 | 811 | 14 |
| GEGL 0.2 | 16.2 | 410 | 43 (compared to vips-c JPEG) |
| Octave 3.8 | 30 (est.) | 8500 (est.) | 200 |
测试环境：
E5-1650 @ 3.20GHz (HP workstation), Ubuntu 15.04
对应的Memory vs. time 曲线图如下：
可以看出，相比于其它库，vips处理速度更快，而且消耗的内存更小，但是比较麻烦的是配置比较麻烦…
提供一个下载链接: http://www.vips.ecs.soton.ac.uk/supported/current/win32/
