Classified residue vector quantization by visual patterns

K. W. Chan; Kwok-Leung Chan

doi:10.1117/12.233240

27 February 1996 Classified residue vector quantization by visual patterns

K. W. Chan, Kwok-Leung Chan

Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233240
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States

Abstract

A new classified residue vector quantization (CRVQ) by visual pattern (VP) without any side information was developed. The original image was first decomposed into a low frequency component (LFC), which was highly correlated with the original, and a residue. The residue was classified not by itself nor the original, but by the LFC. With 15 VPs and 4 variance classes, the visual quality was enhanced from 0.5 to nearly 1.5 dB, without any penalty in bit rate.

Citation Download Citation

K. W. Chan and Kwok-Leung Chan "Classified residue vector quantization by visual patterns", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); https://doi.org/10.1117/12.233240

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