A new paradigm for analysis of MPEG compressed videos

Abstract

The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies for manipulating databases storing these streams. Moreover, content-based access to multimedia databases requires in its first stage to parse the video stream into separate shots then apply a method to summarize the huge amount of data involved in each shot. This work proposes a new paradigm capable of robustly and effectively analyzing the compressed MPEG video data. First, an abstract representation of the compressed MPEG video stream is extracted and used as input to a neural network module (NNM) that performs the shot detection task. Second, we propose two adaptive algorithms to effectively select key frames from segmented video shots produced by the segmentation stage. Both algorithms apply a two-level adaptation mechanism in which the first level is based on the dimension of the input video file while the second level is performed on a shot-by-shot basis in order to account for the fact that different shots have different levels of activity. Experimental results show the efficiency and robustness of the proposed system in detecting shot boundaries and flashlights occurring within shots and in selecting the near optimal set of key frames (KFs) required to represent each shot.