Open AccessAC coefficient and K‐means cuckoo optimisation algorithm‐based segmentation and compression of compound images
Author(s) -
Manju Vethamuthu Nesamony,
Lenin Fred Alfred
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0430
Subject(s) - huffman coding , computer science , artificial intelligence , segmentation , computer vision , compression ratio , image segmentation , data compression , graphics , scale space segmentation , matlab , lossless compression , pattern recognition (psychology) , algorithm , computer graphics (images) , engineering , automotive engineering , internal combustion engine , operating system
Compound images are containing palletise regions including text or graphics and continuous tone images. The compression of compound images is a challenging function and which is complicated to achieve it without degrading the quality of the images. This document is mainly used to improve the compression ratio and an efficient segmentation method is created to separate the background image, text and graphics from the compound images for to make the compression independently. The segmentation is performed through AC coefficient‐based segmentation method resulting in smooth and non‐smooth regions. The non‐smooth region is again segmented by means of K‐means cuckoo optimisation algorithm. In the second phase, the segmented background image, text and graphics were compressed by means of arithmetic coder, Huffman coder and JPEG coder, respectively. This proposed technique is implemented in the working platform of MATLAB and the results were analysed.