On the modeling of DCT and subband image data for compression

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 1995-02, Vol.4 (2), p.186-193
Main Authors: Birney, K.A., Fischer, T.R.
Format: Article
Language:English
Subjects:
Applied sciences
Discrete cosine transforms
Discrete transforms
Entropy
Exact sciences and technology
Gaussian noise
Image coding
Image processing
Information, signal and communications theory
Laplace equations
PSNR
Quantization
Redundancy
Signal processing
Solid modeling
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.< >
ISSN:1057-7149
1941-0042
DOI:10.1109/83.342184