A predictive residual VQ using modular neural network vector predictor

Author

Wang, Lin-Cheng ; Rizvi, Syed A. ; Nasrabadi, Nasser M.

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA

Volume

4

fYear

1997

fDate

21-24 Apr 1997

Firstpage

2953

Abstract

This paper presents a predictive residual vector quantization (PRVQ) scheme using a modular neural network vector predictor. The proposed PRVQ scheme takes the advantage of the high prediction gain and the improved edge fidelity of a modular neural network vector predictor in order to implement a high performance vector quantization (VQ) scheme with low search complexity and a high perceptual quality. Simulation results show that the proposed PRVQ with modular vector predictor outperforms the equivalent PRVQ with general vector predictor (operating at the same bit rate) by more than 1 dB. Furthermore, the perceptual quality of the reconstructed image is also improved

Keywords

image coding; image reconstruction; neural nets; prediction theory; vector quantisation; bit rate; edge fidelity; high perceptual quality; high prediction gain; image coding; low search complexity; modular neural network vector predictor; perceptual quality; predictive residual VQ; reconstructed image; simulation results; vector quantization; Algorithm design and analysis; Educational institutions; Image reconstruction; Laboratories; Milling machines; Neural networks; Performance gain; Powders; Predictive models; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.595409

Filename

595409