A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates

Xiping WANG; Shinji OZAWA

A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates

Xiping WANG, Shinji OZAWA

Full Text Views

0

Share
Cite this

Summary :

This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.

Publication: IEICE TRANSACTIONS on Information Vol.E75-D No.3 pp.342-351

Publication Date: 1992/05/25

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Image Processing, Computer Graphics and Pattern Recognition

Cite this

Copy

Xiping WANG, Shinji OZAWA, "A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates" in IEICE TRANSACTIONS on Information, vol. E75-D, no. 3, pp. 342-351, May 1992, doi: .
Abstract: This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e75-d_3_342/_p

Copy

@ARTICLE{e75-d_3_342,
author={Xiping WANG, Shinji OZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates},
year={1992},
volume={E75-D},
number={3},
pages={342-351},
abstract={This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.},
keywords={},
doi={},
ISSN={},
month={May},}

Copy

TY - JOUR
TI - A Mean-Separated and Normalized Vector Quantizer with Edge-Adaptive Feedback Estimation and Variable Bit Rates
T2 - IEICE TRANSACTIONS on Information
SP - 342
EP - 351
AU - Xiping WANG
AU - Shinji OZAWA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E75-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - May 1992
AB - This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.
ER -