access icon free AC coefficient and K-means cuckoo optimisation algorithm-based segmentation and compression of compound images

© The Institution of Engineering and Technology
Received 10/05/2017, Accepted 20/08/2017, Revised 13/07/2017, Published 25/08/2017

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.

Inspec keywords: arithmetic codes; Huffman codes; data compression; optimisation; image coding; image segmentation

Other keywords: arithmetic coder; nonsmooth region; K-means cuckoo optimisation algorithm; continuous tone images; JPEG coder; palletise regions; Matlab; background image separation; compression ratio; compound image segmentation method; smooth region; compound image compression; image quality; AC coefficient-based segmentation method; Huffman coder

Subjects: Optimisation techniques; Computer vision and image processing techniques; Image and video coding; Optimisation techniques

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