2D-SOFM Vector Quantization for Image Compression Based on Inverse Difference Pyramidal Decomposition

Author

Hikal, Noha A. ; Kountchev, Roumen

Author_Institution

Special Studies Acad., Cairo

Volume

2

fYear

2005

fDate

28-30 Sept. 2005

Firstpage

495

Lastpage

498

Abstract

In this paper a new developed algorithm for compression of still images based on 2D-SOFM NN´s in correspondence with the method of inverse difference pyramid (IDP) decomposition is represented. The new developed algorithm is well suited to be used in progressive image transmission (PIT). Advantage of the method relies on the learning process and adaptation capability of NN´s to reduce the matrices computation complexity and the total number of pyramid levels required for PIT. In addition to, for image reconstruction no interpolation is needed any more, which improves the quality of the reconstructed image

Keywords

image coding; image reconstruction; vector quantisation; visual communication; 2D-SOFM vector quantization; image compression; image reconstruction; inverse difference pyramidal decomposition; learning process; progressive image transmission; Bit rate; Data structures; Discrete cosine transforms; Image coding; Image communication; Image reconstruction; Image resolution; Laplace equations; Spatial resolution; Vector quantization; Image Compression; Inverse Difference Pyramidal Decomposition; SOFM Neural Networks; Vector Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications in Modern Satellite, Cable and Broadcasting Services, 2005. 7th International Conference on

Conference_Location

Nis

Print_ISBN

0-7803-9164-0

Type

conf

DOI

10.1109/TELSKS.2005.1572160

Filename

1572160