Machine-learning-based saliency detection and its video decoding application in wireless multimedia communications

Abstract

Saliency detection has been widely studied to predict human fixations, with various applications in wireless multimedia communications. For saliency detection, we argue that the state-of-the-art high-efficiency video-coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this chapter proposes to learn the video-saliency model, with regard to HEVC features. First, we establish an eye-tracking database for video-saliency detection. Through the statistical analysis on our eye-tracking database, we find out that human fixations tend to fall into the regions with large-valued HEVC features on splitting depth, bit allocation, and motion vector (MV). In addition, three observations are obtained from the further analysis on our eyetracking database. Accordingly, several features in HEVC domain are proposed on the basis of splitting depth, bit allocation, and MV. Next, a support vector machine (SVM) is learned to integrate those HEVC features together, for video-saliency detection. Since almost all video data are stored in the compressed form, our method is able to avoid both the computational cost on decoding and the storage cost on raw data. More importantly, experimental results show that the proposed method is superior to other state-of-the-art saliency-detection methods, either in compressed or uncompressed domain.