Comparison of generalized Gaussian and Laplacian modeling in DCT image coding

Author

Joshi, Rajan L. ; Fischer, Thamas R.

Author_Institution

Sch. of Electr. & Comput. Eng., Washington State Univ., Pullman, WA, USA

Volume

2

Issue

5

fYear

1995

fDate

5/1/1995 12:00:00 AM

Firstpage

81

Lastpage

82

Abstract

Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding. A difference in peak signal to noise ratio (PSNR) of at most 0.5 dB is observed for encoding different images. We also compare maximum likelihood estimation of the generalized Gaussian density parameters with a simpler method proposed by Mallat (1989). With block classification based on AC energy, the densities of the DCT coefficients are much closer to the Laplacian or even the Gaussian.<>

Keywords

Gaussian processes; Laplace equations; discrete cosine transforms; image classification; image coding; maximum likelihood estimation; source coding; DCT coefficients; DCT image coding; PSNR; block classification; discrete cosine transform; generalised Gaussian source models; generalised Laplacian source models; generalized Gaussian density parameters; maximum likelihood estimation; peak signal to noise ratio; Art; Discrete cosine transforms; Image coding; Laplace equations; Maximum likelihood estimation; Parameter estimation; Shape; Statistical analysis; Testing;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.386283

Filename

386283