Image coding 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

1146

Abstract

Image data compression is implemented using a self-supervised backpropagation neural network. First, a backpropagation discrete cosine transform (BPDCT) is developed and then used for transform coding. Secondly, to alleviate edge distortion, classification techniques are applied to transform image coding. The classification technique is based on edge detection since the human visual system is more sensitive to edges. Simulation results show that the BPDCT works better for image coding than a truncated DCT. Classification techniques improve the performance of the BPDCT

Keywords

data compression; encoding; neural nets; picture processing; transforms; backpropagation DCT; backpropagation discrete cosine transform; classification techniques; edge detection; edge distortion; human visual system; image coding; image data compression; self-supervised backpropagation neural network; transform coding; Backpropagation; Data compression; Discrete cosine transforms; Discrete transforms; Humans; Image coding; Image edge detection; Neural networks; Transform coding; Visual system;

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.1991.186627

Filename

186627