Compressed Domain Copy Detection of Scalable SVC Videos

Abstract

We propose a novel approach for compressed domain copy detection of scalable videos stored in a database. We analyze compressed H.264/SVC streams and form different scalable low-level and mid-level feature vectors that are robust to multiple transformations. The features are based on easily available information like the encoding bit rate over time and the motion vectors found in the stream. The focus of this paper lies on the scalability and robustness of the features. A combination of different descriptors is used to perform copy detection on a database containing scalable, SVC-coded High-Definition (HD) video clips.