Title
Linear ARMA Predictors for the Lossless Compression of Two-Dimensional Signals
Abstract
An algorithm for the lossless compression of two-dimensional signals is proposed. This approach is based on modeling the original signal by a rational function which consists of poles and zeros, or equivalently an auto-regressive moving average process. The equation-error structure, which approximates the signal by minimizing the error in the least square sense, is used to obtain the optimal coefficients of the transfer function. This technique is implemented in the frequency domain. The performance of the proposed approach for the lossless compression of different classes of images is evaluated and compared with the lossless linear predictor. The residual sequence of these schemes is coded using arithmetic coding. The suggested approach yields compression measures, in terms of bits per pixel, lower than the lossless linear predictor for compressing 8-bit gray-scale images. © 1997 Academic Press.
Publication Date
1-1-1997
Publication Title
Digital Signal Processing: A Review Journal
Volume
7
Issue
2
Number of Pages
120-126
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1006/dspr.1997.0285
Copyright Status
Unknown
Socpus ID
0031122304 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0031122304
STARS Citation
Nijim, Yousef W.; Stearns, Samuel D.; and Mikhael, Wasfy B., "Linear ARMA Predictors for the Lossless Compression of Two-Dimensional Signals" (1997). Scopus Export 1990s. 2825.
https://stars.library.ucf.edu/scopus1990/2825