Sparse Decomposition Technique for Segmentation and Compression of Compound Images

Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under blo...

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Bibliographic Details
Published in:Journal of intelligent systems 2020-01, Vol.29 (1), p.515-528
Main Authors: Manju, V.N., Lenin Fred, A.
Format: Article
Language:English
Subjects:
Algorithms
Coding
Compound images
Compression ratio
Decomposition
Huffman codes
Image classification
Image compression
Image quality
Image segmentation
Optimization
segmentation
sparse decomposition
Wavelet analysis
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Summary:Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under block-based classification to distinguish the compound image segments. The segmentation process starts with separation of the entire image into blocks by spare decomposition technique in smooth blocks and non smooth blocks. Gray wolf-optimization based FCM (fuzzy C-means) algorithm is employed to segment background, text, graphics, images and overlap, which are then individually compressed using adaptive Huffman coding, embedded zero wavelet and H.264 coding techniques. Exploratory outcomes demonstrate that the proposed conspire expands compression ratio, enhances image quality and additionally limits computational complexity. The proposed method is implemented on the working platform of MATLAB.
ISSN:0334-1860
2191-026X
DOI:10.1515/jisys-2017-0360