Improving the performance of MPEG compatible encoders using on line retrainable neural networks

Author

Kollias, Stefanos ; Doulamis, Nikolaos ; Doulamis, Anastasios

Author_Institution

Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece

Volume

3

fYear

1997

Firstpage

424

Abstract

On line retraining of neural network is introduced for extracting foreground/background objects in video sequences. The scheme is applied together with a modification of the rate control of MPEG-1 algorithm. The proposed method is compatible to MPEG-1/2 standard but also can be used as a pre-coding stage for the forthcoming MPEG-4 algorithm. Simulation studies have shown an improvement of about 1.5 dB on average as far the PSNR is concerned compared with the conventional MPEG-1 encoder

Keywords

code standards; feature extraction; image sequences; learning (artificial intelligence); neural nets; real-time systems; telecommunication standards; video coding; MPEG compatible encoders; MPEG-1 algorithm; MPEG-1 encoder; MPEG-1/2 standard; MPEG-4 algorithm; PSNR; foreground/background objects extraction; online retrainable neural networks; performance; precoding stage; rate control; simulation; video sequences; Computer networks; Decoding; Humans; Image coding; Image quality; Image segmentation; MPEG 4 Standard; Neural networks; Transform coding; Videoconference;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1997. Proceedings., International Conference on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-8186-8183-7

Type

conf

DOI

10.1109/ICIP.1997.632146

Filename

632146