CALIC - a context based adaptive lossless image codec

Xiaolin Wu, Nasir Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages1890-1893
Number of pages4
Volume4
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: May 7 1996May 10 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6)
CityAtlanta, GA, USA
Period5/7/965/10/96

Fingerprint

Data structures
Statistics
Feedback
predictions
coding
Error probability
estimating
statistics
estimates

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

Cite this

Wu, X., & Memon, N. (1996). CALIC - a context based adaptive lossless image codec. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 4, pp. 1890-1893). IEEE.

CALIC - a context based adaptive lossless image codec. / Wu, Xiaolin; Memon, Nasir.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 IEEE, 1996. p. 1890-1893.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, X & Memon, N 1996, CALIC - a context based adaptive lossless image codec. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 4, IEEE, pp. 1890-1893, Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6), Atlanta, GA, USA, 5/7/96.
Wu X, Memon N. CALIC - a context based adaptive lossless image codec. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4. IEEE. 1996. p. 1890-1893
Wu, Xiaolin ; Memon, Nasir. / CALIC - a context based adaptive lossless image codec. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 IEEE, 1996. pp. 1890-1893
@inproceedings{786a547a57e54d168d4f6f2fc1848b3a,
title = "CALIC - a context based adaptive lossless image codec",
abstract = "We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.",
author = "Xiaolin Wu and Nasir Memon",
year = "1996",
language = "English (US)",
volume = "4",
pages = "1890--1893",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",

}

TY - GEN

T1 - CALIC - a context based adaptive lossless image codec

AU - Wu, Xiaolin

AU - Memon, Nasir

PY - 1996

Y1 - 1996

N2 - We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.

AB - We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.

UR - http://www.scopus.com/inward/record.url?scp=0029725952&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029725952&partnerID=8YFLogxK

M3 - Conference contribution

VL - 4

SP - 1890

EP - 1893

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

PB - IEEE

ER -