Media Quality Assessment by Perceptual Gaze-Shift Patterns Discovery.

Quality assessment is an indispensable technique in a large body of media applications, i.e., photo retargeting, scenery rendering, and video summarization. In this paper, a fully automatic framework is proposed to mimic how humans subjectively perceive media quality. The key is a locality-preserved sparse encoding algorithm that accurately discovers human gaze shifting paths from each image or video clip. In particular, we first extract local image descriptors from each image/video, and subsequently project them into the so-called perceptual space. Then, a nonnegative matrix factorization (NMF) algorithm is proposed that represents each graphlet by a linear and sparse combination of the basis ones. Since each graphlet is visually/semantically similar to its neighbors, a locality-preserved constraint is encoded into the NMF algorithm. Mathematically, the saliency of each graphlet is quantified by the norm of its sparse codes. Afterward, we sequentially link them into a path to simulate human gaze allocation. Finally, a probabilistic quality model is learned based on such paths extracted from a collection of photos/videos, which are marked as high quality ones via multiple Flickr users. Comprehensive experiments have demonstrated that: 1) our quality model outperforms many of its competitors significantly, and 2) the learned paths are on average 89.5% consistent with real human gaze shifting paths.