Inverse error-diffusion using classified vector quantization

Author

Lai, Jim Z C ; Yen, J.Y.

Author_Institution

Dept. of Inf. Eng., Feng Chia Univ., Taichung, Taiwan

Volume

7

Issue

12

fYear

1998

fDate

12/1/1998 12:00:00 AM

Firstpage

1753

Lastpage

1758

Abstract

This correspondence extends and modifies classified vector quantization (CVQ) to solve the problem of inverse halftoning. The proposed process consists of two phases: the encoding phase and decoding phase. The encoding procedure needs a codebook for the encoder which transforms a halftoned image to a set of codeword-indices. The decoding process also requires a different codebook for the decoder which reconstructs a gray-scale image from a set of codeword-indices. Using CVQ, the reconstructed gray-scale image is stored in compressed form and no further compression may be required. This is different from the existing algorithms, which reconstructed a halftoned image in an uncompressed form. The bit rate of encoding a reconstructed image is about 0.51 b/pixel

Keywords

decoding; image coding; image reconstruction; inverse problems; vector quantisation; classified vector quantization; codeword-indices; decoding phase; encoding phase; gray-scale image reconstruction; halftoned image; inverse error-diffusion; inverse halftoning; Decoding; Encoding; Gray-scale; Image coding; Image reconstruction; Maximum likelihood detection; Nonlinear filters; Pixel; Smoothing methods; Vector quantization;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.730390

Filename

730390