Open Access Open Access  Restricted Access Subscription or Fee Access

Scanned Compound Document Image Compression using Optimization Techniques

K. Uma, P. Geetha, A. Kannan

Abstract


In Existing systems, compound document compression follow a top down approach that first segments the original image into three distinct layers ,the background layer containing smooth objects, the foreground layer containing information regarding text and graphics colors and mask layer by using the MRC model. These distinct layers are then compressed individually by various encoders like JPEG2000, H.264/AVC Intra, and Digipaper, MMP algorithm to compress the document. In this paper, the existing scheme uses a block based classification that separates pictorial macro-blocks in to text and graphics ones and the macro-blocks are signalled using a binary mask. Each of these two types of macro-blocks are then encoded using a version of the Multidimensional Multiscale Parser (MMP) algorithm specifically designed to encode it. The non-smooth blocks (text/graphics) are compressed using MMP-text algorithm and the smooth blocks are compressed by means of MMP-FP (flexible Partition (FP) algorithm. In proposed scheme, Macro blocks are segmented with the use of morphological filters and its separated blocks are using R-D optimization techniques. The Modified MMP algorithms are implemented to efficiently encode the text blocks and the image blocks. For encoding text blocks Modified MMP-Text algorithm is used, where the binary tree based segmentation process is implemented. For encoding the image blocks, Modified MMP-FP algorithm is implemented, where Quad-tree based segmentation process is carried out. Modified MMP has an advantage that it is not sensitive to pixel classification errors. Also it increases the compression ratio, improves the compression effectiveness, improves the image quality, reduces the search space between the pixels and also minimizes the computational complexity.

Keywords


Morphological Operation, R-D Optimization, Modified MMP Compression, Modified MMP-FP Compression, Dictionary Creation, Pattern Matching.

Full Text:

PDF

References


Amir said, William.A.Pearlman,”A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Transactions on circuits and systems for video technology, vol. 6, no. 3, June 1996.

Amir Said and Alex Drukarev, Simplified Segmentation for Compound Image Compression, Hewlett-Packard Laboratories, Palo Alto, CA.

Cuiling Lan, Guangming Shi, and Feng Wu,”Compress Compound Images in H.264/MPGE-4 AVC by Exploiting Spatial Correlation,” IEEE Transactions on image processing, vol. 19, no. 4, April 2010.

Daniel Huttenlocher and Pedro Felzenszwalb, William Rucklidge, Digipaper: A Versatile Color Document Image Representation,Dept. of Computer Science,Cornell University.,Ithaca, NY 14853.

Danillo B. Graziosi, Nuno M. M. Rodrigues, Carla L. Pagliari,Eduardo A. B. da Silva3, S´ergio M. M. de Faria, Marcelo M. Perez, Murilo B. de Carvalho, Multiscale Recurrent Pattern Matching Approach For Depth Map Coding, Instituto de Telecomunicac¸ ˜oes;2ESTG, Instituto Polit´ecnico de Leiria, Portugal,in 2008.

D. Huttenlocher, P. Felzenszwalb, and W. Rucklidge, “Digipaper: A versatile color document image representation,” in Proc. IEEE Int. Conf. Image Processing, Kobe, Japan, 1999, pp. 219–223.

D Mukherjee,”JPEG2000-Matched MRC Compression of compound documents,”Compression and Multimedia Technologies Group IEEE ICIP 2002.

Dong Liu, Wenpeng Ding, Yuwen He, Feng Wu, Quality-biased Rate Allocation for Compound Image Coding with Block Classification,Dept. of Computer Science, University of Science and Technology of China, Hefei 230026, China.

Eri haneda, Charles A.Bouman,”Text segmentation for MRC document compression,” IEEE transactions on image processing, vol. 20, no. 6, June 2011.

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vis. Comput., vol. 25, no. 6, pp. 883– 889,2007.

Gregory K. Wallace,”The JPEG Still Picture Compression Standard,”Multimedia Engineering Digital Equipment Corporation Maynard, Massachusetts Submitted in December 1991 for publication in IEEE Transactions on Consumer Electronics.

Gauotong feng, Charles.A,”High-Quality MRC Document Coding,”IEEE Transactions on image processing, vol. 15, no. 10, October 2006.

Hui Chenga, Guotong Fengb and Charles A. Bouman, Rate-Distortion Based Segmentation for MRC Compression, Purdue University, West Lafayette, IN 47907-1285, USA.

Julien Reichel, Gloria Menegaz, Marcus J. Nadenau and Murat Kunt, Integer Wavelet Transform for Embedded Lossy to Lossless Image Compression, Signal Processing Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland.

Jin-Sung,Kim ,Joohyuk Yum ,Hyuk-Jae Lee, “Block-Based Adaptive Noise Filtering for H.264/AVC Compression,” IEEE Transactions on Consumer Electronics, Vol. 57, No. 3, August 2011.

L. Lucas, N. Rodrigues, and S. Faria, “Compound image segmentation for multiscale recurrent pattern coding,” in Proc.Int.Conf.Telecommunications, May 2009, pp. 267–270.

Marcel Wagner and Dietmar Saupe, RD-Optimization of Hierarchical Structured Adaptive Vector Quantization for Video Coding,Universit¨at Freiburg, Am Flughafen 17, 79110 Freiburg, Germany.

Nelson C. Francisco,Nuno M. M. Rodrigues, Eduardo A. B. da Silva,,”Multiscale recurrent pattern image coding with flexible partition scheme,”.at Aug 2008.

Nelson.C.Francisco,Nuno.M.M.Rodrigues,Eduardo,A.B.da Silva,MuriloBresciani de Carvalho,Sergio M.M.de Faria and Vitor M.M.Silva,”Scanned Compound Document Encoding Using Multiscale Recurrent Patterens,”IEEE Trans.Image Process.,Vol 19,no.10, Oct.2010,pp.

Marco Grangetto, Enrico Magli, Maurizio Martina, and Gabriella Olmo,”Optimization and implementation of the integer wavelet transform for image coding,” IEEE Transactions on image processing vol 11.no.6.June 2002.

P. Haffner, L. Bottou, P. G. Howard, and Y. L. Cun, “Djvu : Analyzing and compressing scanned documents for internet distribution,” in Proc. Int. Conf. Document Analysis and Recognition, 1999, pp. 625–628.

Wenpeng Ding*a, Yan Lub, Feng Wub, Shipeng Lib, Rate-distortion optimized color quantization for compound image compression in Dept. of Computer Science, University of Science and Technology of China, Hefei 230027, China ,Microsoft Research Asia, Beijing, 100080, China

Zaghetto and R. L. de Queiroz, “Iterative pre- and post-processing for MRC layers of scanned documents,” inProc. IEEE Int. Conf. Image Processing, CA, Oct. 2008, pp. 1009–1012.

Zhigang Pan,Xin Gao,Xiumin Sun, Hongmei Song ,”A Lossless Compression Algorithm for SAR Amplitude Imagery Based on Modified Quadtree Coding of Bit Plane,” IEEE Geoscience and remote sensing letters, vol. 7, no. 4, October 2010.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.