A spatiotemporal most-apparent-distortion model for video quality assessment
This paper presents an algorithm for video quality assessment, spatiotemporal MAD (ST-MAD), which extends our previous image-based algorithm (MAD [1]) to take into account visual perception of motion artifacts. ST-MAD employs spatiotemporal “images” (STS images [2]) created by taking time-based slices of the original and distorted videos. Motion artifacts manifest in the STS images as spatial artifacts, which allows one to quantify motion-based distortion by using classical image-quality assessment techniques. ST-MAD estimates motion-based distortion by applying MAD's appearance-based model to compare the distorted video's STS images to the original video's STS images. This comparison is further adjusted by using optical-flow-derived weights designed to give greater precedence to fast-moving regions located toward the center of the video. Testing on the LIVE video database demonstrates that ST-MAD performs well in predicting video quality.