Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme

Rin Chul Kim; Sang Uk Lee

doi:10.1117/12.131464

1 November 1992 Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme

Rin Chul Kim, Sang Uk Lee

Author Affiliations +

Proceedings Volume 1818, Visual Communications and Image Processing '92; (1992) https://doi.org/10.1117/12.131464
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

In this paper, an entropy constrained predictive vector quantizer (ECPVQ) for image coding is described, and an adaptive ECPVQ (AECPVQ) technique to take into account the local characteristics of the input image is proposed. The adaptation is achieved by employing a classifier and the variable vector dimension scheme. In the proposed AECPVQ coder, separate predictors and codebooks are prepared for each class. The 6 X 6 input block is classified into one of the predetermined 6 classes according to the distribution of the feature vector in the DCT domain. Then, the input block is partitioned into several small vectors by the proposed variable vector dimension scheme to take into account the orientation of edge and the variances for each class. The vectors in each class are encoded using the corresponding codebook and the predictor. The computer simulation result shows that the proposed AECPVQ outperforms the conventional ECPVQ in terms of both the subjective quality and peak signal to noise ratio. For example, the AECPVQ enjoys a 1.5 dB gain over the ECPVQ at 0.7 bits/pel on the Lena image.

Citation Download Citation

Rin Chul Kim and Sang Uk Lee "Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme", Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); https://doi.org/10.1117/12.131464

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