On the modeling of DCT and subband image data for compression

Author

Birney, K.A. ; Fischer, Thomas R.

Author_Institution

Aerosp. & Defense Sector, Hughes Aircraft Co., Fullerton, CA, USA

Volume

4

Issue

2

fYear

1995

fDate

2/1/1995 12:00:00 AM

Firstpage

186

Lastpage

193

Abstract

Image subband and discrete cosine transform coefficients are modeled for efficient quantization and noiseless coding. Quantizers and codes are selected based on Laplacian, fixed generalized Gaussian, and adaptive generalized Gaussian models. The quantizers and codes based on the adaptive generalized Gaussian models are always superior in mean-squared error distortion performance but, generally, by no more than 0.08 bit/pixel, compared with the much simpler Laplacian model-based quantizers and noiseless codes. This provides strong motivation for the selection of pyramid codes for transform and subband image coding

Keywords

Gaussian processes; data compression; discrete cosine transforms; image coding; quantisation (signal); DCT; Laplacian models; adaptive generalized Gaussian models; compression; discrete cosine transform; fixed generalized Gaussian models; mean-squared error distortion performance; noiseless coding; pyramid codes; quantization; subband image coding; subband image data; transform image coding; Discrete cosine transforms; Discrete transforms; Entropy; Gaussian noise; Image coding; Laplace equations; PSNR; Quantization; Redundancy; Solid modeling;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.342184

Filename

342184