Hybrid vector quantization

Kuei-Ann Wen; Chung-Yen Lu

doi:10.1117/12.141682

1 July 1993 Hybrid vector quantization

Kuei-Ann Wen, Chung-Yen Lu

Author Affiliations +

Optical Engineering, Vol. 32, Issue 7, (July 1993). https://doi.org/10.1117/12.141682

Abstract

Vector quantization (VQ) is a powerful technique for low-bit-rate image coding. However, initial studies of image coding with VQ have revealed that VQ causes degradations, most notably around edges. Moreover, the computational complexity is high. Although a few algorithms have been developed to reduce edge degradation, such as block truncation coding (BTC) with VQ (BTCNQ) or classified vector quantization, their compression ratios are not satisfactory. Discrete cosine transformation with VQ (DCTNQ) has been applied to image compression, showing a high compression ratio, but the edge degradation problem still exists. We present an image compression algorithm that takes advantage of the merits of DCTNQ and BTCNQ to achieve a high-quality and low-bit-rate compression of images. High quality images can be achieved at rates of 0.34 to 0.46 bit/pixel.

Citation Download Citation

Kuei-Ann Wen and Chung-Yen Lu "Hybrid vector quantization," Optical Engineering 32(7), (1 July 1993). https://doi.org/10.1117/12.141682

Published: 1 July 1993

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