Abstract.
Video sequences are major sources of traffic for broadband ISDN networks, and video compression is fundamental to the efficient use of such networks. We present a novel neural method to achieve real-time adaptive compression of video. This tends to maintain a target quality of the decompressed image specified by the user. The method uses a set of compression/decompression neural networks of different levels of compression, as well as a simple motion-detection procedure. We describe the method and present experimental data concerning its performance and traffic characteristics with real video sequences. The impact of this compression method on ATM-cell traffic is also investigated and measurement data are provided.
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Gelenbe, E., Sungur, M., Cramer, C. et al. Traffic and video quality with adaptive neural compression . Multimedia Systems 4, 357–369 (1996). https://doi.org/10.1007/s005300050037
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DOI: https://doi.org/10.1007/s005300050037