Abstract
Many algorithms for temporal video partitioning rely on the analysis of uncompressed video features. Since the information relevant to the partitioning process can be extracted directly from the MPEG compressed stream, higher efficiency can be achieved utilizing information from the MPEG compressed domain. This paper introduces a real-time algorithm for scene change detection that analyses the statistics of the macroblock features extracted directly from the MPEG stream. A method for extraction of the continuous frame difference that transforms the 3D video stream into a 1D curve is presented. This transform is then further employed to extract temporal units within the analysed video sequence. Results of computer simulations are reported.
Highlights
The development of highly efficient video compression technology combined with the rapid increase in desktop computer performance, and a decrease in the storage cost, have led to a proliferation of digital video media
We propose to extract MBType information from the MPEG stream and to use it to measure the “amount” of interframe reference
The collection of C++ classes called MPEG development classes, implemented by Dongge and Sethi [15], are used as the main tool for manipulating the MPEG streams, while Berkeley mpeg2codec was used as the reference MPEG codec
Summary
The development of highly efficient video compression technology combined with the rapid increase in desktop computer performance, and a decrease in the storage cost, have led to a proliferation of digital video media. Algorithms in the uncompressed domain utilize features extracted from the spatial video domain: pixel-wise difference [4], histograms [5], edge tracking [6], and so forth These techniques are computationally demanding and time-consuming, and inferior to the approach based on the compressed domain analysis. A step forward was done by Pei and Chou [13] where they matched patterns of macroblocks (MB) types within abrupt or gradual change with the expected shapes combining it with partially spatial information These methods have not shown realtime processing capabilities and none of them generated continuous output, essential for further scalable analysis.
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