The sum of absolute differences may be used for a variety of purposes, such as object recognition, the generation of disparity maps for stereo images, and motion estimation for video compression.
The sum of absolute differences provides a simple way to automate the searching for objects inside an image, but may be unreliable due to the effects of contextual factors such as changes in lighting, color, viewing direction, size, or shape.
The SAD may be used in conjunction with other object recognition methods, such as edge detection, to improve the reliability of results.
For example, SSE has packed sum of absolute differences instruction (PSADBW) specifically for this purpose.
Once candidate blocks are found, the final refinement of the motion estimation process is often done with other slower but more accurate metrics, which better take into account human perception.