Adaptive encoding based lossless data hiding method for VQ compressed images using tabu search

A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to r...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences Vol. 602; pp. 128 - 142
Main Authors: Zhang, Tiancong, Weng, Shaowei, Wu, Zhijie, Lin, Juan, Hong, Wien
Format: Journal Article
Language:English
Published: Elsevier Inc 01.07.2022
Subjects:
Index rearrangement
Linear regression method
Reversible data hiding
Tabu search algorithm
Vector quantization
Linear regression method
Index rearrangement
Reversible data hiding
Vector quantization
Tabu search algorithm
ISSN:0020-0255, 1872-6291
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to reduce the required bitrate. In this study, the tabu search algorithm is employed to rearrange codewords by fully exploiting their neighboring correlations, yielding a moepressible rearranged indices. By combining more highly-correlated indices of a to-be-predicted index into prediction, the improved linear regression method is then applied to achieve a sharper prediction-error histogram and less required additional information. After prediction, an adaptive run-length encoding method is presented to encode prediction errors, thereby eliminating unnecessary indicators. Experimental results demonstrate that the proposed method effectively reduces the bitrate of the compressed image while providing a comparable hiding capacity.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.04.011