AC coefficient and K‐means cuckoo optimisation algorithm‐based segmentation and compression of compound images. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Manju, Vethamuthu Nesamony; Lenin Fred, Alfred
• Is Part Of: IET image processing. Volume 12:Issue 2(2018)
• Page Start: 218
• Page End: 225
• Publication Date: 2018-02-01
• Subjects: image coding -- data compression -- optimisation -- image segmentation -- arithmetic codes -- Huffman codes; K‐means cuckoo optimisation algorithm -- compound image compression -- compound image segmentation method -- palletise regions -- continuous tone images -- image quality -- compression ratio -- background image separation -- AC coefficient‐based segmentation method -- nonsmooth region -- smooth region -- arithmetic coder -- Huffman coder -- JPEG coder -- Matlab; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0430
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.