Text-Image Segmentation and Compression using Adaptive Statistical Block Based Approach
Nidhal Kamel Taha El-Omari1, Ahmad H. Al-Omari2, Ali Mohammad H. Al-Ibrahim3, Tariq Alwada’n4

1Nidhal Kamel Taha El-Omari, WISE University, Faculty of Information Technology, Amman, Jordan.
2Ahmad H. Al-Omari, Northeren Border University, Faculty of Science, Computer Science Division, Saudi Arabia.
3Ali Mohammad H. Al-Ibrahim, WISE University, Faculty of Information Technology, Amman, Jordan.
4Tariq Alwada’n, WISE University, Faculty of Information Technology, Amman, Jordan, tariq.

Manuscript received on 13 April 2017 | Revised Manuscript received on 20 April 2017 | Manuscript Published on 30 April 2017 | PP: 1-9 | Volume-6 Issue-4, April 2017 | Retrieval Number: D4873046417/17©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Images and scanned text documents are gradually more used in a vast range of applications. To reduce the needed storage or to accelerate their move through the computers networks, the document images have to be compressed. Traditional compression mechanisms, which are generally developed with a particular image type and purpose, are facing many challenges with mixed documents. This paper describes a statistical block-based technique for an automatic document image segmentation and compression. Based on the number of detected colors in each region of the image, this approach creates a new representation of the image that can produce very highly-compressed document files that nonetheless retain excellent image quality. The proposed algorithm segments the compound document image into blocks of equal size. The blocks are classified into seven different categories. Each category represents an image part that shares the same properties. A new representation of each category is formed and the similar adjacent blocks are merged to form labeled regions sharing the same properties. At the end, to achieve better compression ratio, the different regions of the image are compressed using different compression techniques.
Keywords: Adaptive Compression, Block-Based Segmentation, Image Document Compression, Image Segmentation, Lookup Dictionary Table (LUD).

Scope of the Article: Image Processing