DocumentCode
2908019
Title
Progressive image transmission using self-supervised backpropagation neural network
Author
Gong, Wei ; Rao, K.R. ; Manry, M.T.
Author_Institution
Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
fYear
1991
fDate
4-6 Nov 1991
Firstpage
1133
Abstract
A novel technique for progressive image transmission (PIT) is presented which uses a self-supervised backpropagation neural network discrete cosine transform. The transmission sequence is determined using a backpropagation neural network (BPNN) feature importance function. Simulation results show that the PIT system can be successfully implemented using BPNN. Very good intermediate images are obtained at reasonable bit rates
Keywords
neural nets; picture processing; transforms; visual communication; DCT; bit rates; discrete cosine transform; feature importance function; image processing; intermediate images; progressive image transmission; self-supervised backpropagation neural network; transmission sequence; visual telecommunication services; Backpropagation; Bit rate; Discrete cosine transforms; Electronic mail; HDTV; Image communication; Kernel; Medical simulation; Neural networks; Teleconferencing;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
Conference_Location
Pacific Grove, CA
ISSN
1058-6393
Print_ISBN
0-8186-2470-1
Type
conf
DOI
10.1109/ACSSC.1991.186624
Filename
186624
Link To Document