Lossless predictive coding of color graphics

Gregory S. Yovanof; James R. Sullivan

doi:10.1117/12.58316

19 May 1992 Lossless predictive coding of color graphics

Gregory S. Yovanof, James R. Sullivan

Proceedings Volume 1657, Image Processing Algorithms and Techniques III; (1992) https://doi.org/10.1117/12.58316
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

Abstract

General purpose image compression algorithms do not fully exploit the redundancy of color graphical images because the statistics of graphics differ substantially from those of other types of images, such as natural scenes or medical images. This paper reports the results of a study of lossless predictive coding techniques specifically optimized for the compression of computer generated color graphics. In order to determine the most suitable color representation space for coding purposes the Karhunen-Loeve (KL) transform was calculated for a set of test images and its energy compaction ability was compared with those of other color spaces, e.g., the RGB, or the YUV signal spaces. The KL transform completely decorrelates the input color data for a given image and provides a lower bound on the color entropy. Based on the color statistics measured on a corpus of test images a set of optimal spatial predictive coders were designed. These schemes process each component channel independently. The prediction error signal was compressed by both lossless textual substitutional codes and statistical codes to achieve distortionless reproduction. The performance of the developed schemes is compared with that of the lossless function of the JPEG standard.

Citation Download Citation

Gregory S. Yovanof and James R. Sullivan "Lossless predictive coding of color graphics", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); https://doi.org/10.1117/12.58316

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available