Image data compression and generalization capabilities of backpropagation and recirculation networks

Huang, Shuo; Koh, Seong-Ryong; Tang, Haitong

doi:10.1109/iscas.1991.176690

Cited by 5 publications

(1 citation statement)

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“…A backpropagation network to compress data at the hidden layer and an implementation on a 512 processor NCUBE are discussed by Sonehara (1989). Huang (1991) compares backpropagation networks with recirculation networks and the DCT. The best results reported here are obtained with the DCT, then with recirculation networks and finally with backpropagation networks.…”

Section: Previous Workmentioning

confidence: 99%

Traffic and video quality with adaptive neural compression

et al. 1996

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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 motiondetection 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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Section: Previous Workmentioning

confidence: 99%