Abstract
This paper presents an adaptive rate-constrained key-frame selection scheme for channel-aware realtime video streaming applications. The proposed method dynamically determines the target number of key-frames by estimating the channel conditions according to feedback information. A two-step sequential key-frame selection scheme is then utilized to select a target number of key-frames by first finding the optimal allocation of key-frame budget among the video shots in a video clip using an analytical model, and then selecting most representative key-frames in each shot according to the allocation. The feature information used for key-frame selection is extracted offline and stored in the server as metadata for realtime streaming and transcoding. Experimental results show that the proposed method can achieve good performance with acceptable complexity. © 2004 IEEE.