Combined techniques of singular value decomposition and vector quantization for image coding

Author

Yang, Jar-Ferr ; Lu, Chjou-hang

Author_Institution

Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan

Volume

4

Issue

8

fYear

1995

fDate

8/1/1995 12:00:00 AM

Firstpage

1141

Lastpage

1146

Abstract

The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity

Keywords

decoding; image coding; singular value decomposition; transform coding; vector quantisation; DCT; SVD; VQ; algorithms; codebook; data rate; decoding complexity; discrete cosine transform; energy compaction; high quality image coding; image quality; low bit rate image coding; simulation results; singular value decomposition; singular vectors; transform coding; vector quantization; Bit rate; Compaction; Computational modeling; Decoding; Discrete cosine transforms; Image coding; Image quality; Matrix decomposition; Singular value decomposition; Vector quantization;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.403419

Filename

403419