DPCM with a recurrent neural network predictor for image compression

Author

Park, Dong-Chul ; Park, Tae-Hoon

Author_Institution

Sch. of Electr. & Electron. Eng., MyongJi Univ., South Korea

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

826

Abstract

A new predictor for DPCM based on a recurrent neural network is proposed. The proposed predictor which uses the bilinear recurrent neural network (BLRNN), has shown good performance for time-series prediction problems, and it is applied to DPCM for image compression with predictive coding. The performance of DPCM with BLRNN predictor is compared with the conventional DPCM with different predictors, such as the linear predictor and median predictor. The results show that the proposed method gives improved results over the conventional DPCM with linear predictor or median predictor in terms of PSNR or reconstructed images

Keywords

data compression; differential pulse code modulation; image processing; linear predictive coding; recurrent neural nets; time series; DPCM coding; bilinear recurrent neural network; differential pulse code modulation; image compression; image processing; predictive coding; time-series; Decoding; Image coding; Image reconstruction; Modulation coding; Neural networks; Neurons; Pixel; Predictive coding; Pulse modulation; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.685874

Filename

685874