Designing a Codec System for High Resolution Textual Images Based on Super Resolution

Document Type : Original Article


1 Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

2 Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran,


In this paper, a CODEC system based on super resolution, is proposed for compression of high resolution textual images. It employs image resizing to decrease image dimensions and consequently, to improve the compression ratio; but at the other hand, it may reduce the image quality. Therefore, the decompression unit employs super resolution to simultaneously increase the reconstructed image dimensions and quality. In the employed interpolation-based super-resolution method, using an efficient textual image matting algorithm, the input low-resolution textual image is decomposed into three layers after which, each layer is enlarged using a particular method. Finally, the enlarged layers are combined to build the high resolution reconstructed textual image. An interesting feature of the proposed method is the ability to use existing compression methods such as JPEG, JPEG2000 and SPIHT. We have employed the aforementioned compression methods in the proposed CODEC system and evaluated the compression results with respect to OCR rate, Mean Opinion Score (MOS), and PSNR measures. Considering the OCR and MOS measures, the proposed method outperformed the others but not so with respect to PSNR.


Main Subjects

[1] W. Naaman, “Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review”,AJRCoS, vol. 10, no. 4, pp. 47-57, Jul. 2021.
[2] H Huda, “Lossless Text Image Compression using Two Dimensional Run Length Encoding”, Journal Online Anformatika (JOIN), vol. 4, n0. 2, pp.75-78, 2019.
[3] Grailu, M. Lotfizad and H. S. Yazdi, “Farsi and arabic document images lossy compression based on the mixed raster content model,” International Journal on Document Analysis and Recognition (IJDAR), vol. 12, no. 4, pp. 227-248, 2009.
[4] C. Francisco, N. M. M. Rodrigues, E. A. B. Silva, M. B. Carvalho, S. M. M. Faria and V. M. M. Silva, “Scanned compound document encoding using multiscale recurrent patterns,” IEEE Transactions on Image Processing, vol. 19, no. 10, pp. 2712-2724, 2010.
[5] H. Sharpe and B. Manns, “JPEG 2000 options for document image compression,” Document Recognition and Retrieval IX, Paul B. Kantor, Tapas Kanungo, Jiangying Zhou, Editors, Proceedings of SPIE, vol. 4670, pp. 167-173, 2002.
[6] Dhawan, “A review of image compression and comparison of its algorithms,” International Journal of Electronics and Communication Technology, vol. 2, no. 1, pp. 22-26, 2011.
[7] A. Pearlman and A. Said, “Set partitioning coding: part I of set partition coding and image wavelet coding systems”, Foundations and Trends in Signal Processing, vol. 2, no. 2, pp. 95-180, 2008.
[8] A. Pearlman and A. Said, “Image wavelet coding systems: part II of set partition coding and image wavelet coding systems,” Foundations and Trends in Signal Processing, vol. 2, no. 3, pp. 181-246, 2008.
[9] A. Pearlman and A. Said, Digital Signal Compression: Principles and Practice, Cambridge University Press, New York, 2011.
[10] Said and W. 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, pp. 243-250, 1996.
[11] Grailu, ‘‘Textual Image Compression for Maintaining or Improving the Recognition Performance’’, Circuits Syst Signal Process, no. 36, pp. 658–674, 2017.
[12] Mahesh, P. Rajesh and I. Suneetha, “Improved block based segmentation for JPEG compressed document images”, International Journal of Research in Engineering and Technology, vol. 2, no. 11, pp. 669-673, 2013.
[13] Oztan, A. Malik, Z. Fan and R. Eschbach, “Removal of artifacts from JPEG compressed document images”, Proceedings of SPIE-IS&T Electronic Imaging, vol. 6493, pp. 649306-1: 649306-9, 2007.
[14] Zaghetto and R. L.Queiroz, “Scanned document compression using a block-based hybrid video codec”, IEEE Transactions on Image Processing, vol. 22, no. 6, pp. 2420-2428, 2010.
[15] Grailu, “Textual image compression at low bit rates based on region-of-interest coding”, IJDAR,no. 19, pp. 65-81, 2016.
[16] Taubman, “High performance scalable image compression with EBCOT”, IEEE Transactions on Image Processing, vol. 9, no. 7, pp. 1151-1170, 2000.
[17] Lim, Y. Liu, T.H. Li, Sh. Liu, G. Li, , “Text Image Super-Resolution by Image Matting and Text Label Supervision”, Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2019.
[18] Ma, Sh. Guo, L. Zhang, “Text Prior Guided Scene Text Image Super-resolution”, arXiv:2106.15368, 2021.
[19] Cai, et al, ‘‘Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model’’, arXiv:1904.00523, 2019.
[20] C. Chiang and T. E. Boulte, “Efficient super-resolution via image warping”, Image and Vision Computing, vol. 18, no. 10, pp. 761-771, 2000.
[21] Farsiu, M. D. Robinson, M. Elad and P. Milanfar, “Fast and robust multiframe super resolution”, IEEE Transactions on Image processing, vol. 13, pp. 1327-1344, 2004.
[22] Fattal, “Image upsampling via imposed edges statistics”, ACM Transaction on Graphics, vol. 26, no. 3, pp. 95:1-95:8, 2007.
[23] Shan, et al., “Fast image/video upsampling”, ACM Transaction on Graphics, vol. 25, no. 5, pp. 153:1-153:7, 2008.
[24] Levin, et al., “A closed-form solution to natural image matting”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 228-242, 2008.
[25] zheng, X.KangShatoLi and Y.HeJunSun, "Real-time document image super-Resolution by fast matting" 11th IAPR International Workshop on Document Analysis System, 2014.
[26] Wang and F. Cohen, “Optimized color sampling for robust matting”, International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[27] He, J. Sun, X. Tang, “Guided image filtering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, 2013.
[28] Khangar S. V. and Malik L. G., Handwritten Text Image Compression for Indic Script Document, 2012, International Journal of Computer Applications, Vol. 47, No. 5, pp. 11-16.
[29] de Queiroz R., Buckley R. and Xu M., Mixed Raster Content (MRC) Model for Compound Image Compression, Proc. IS&T/SPIE Symposium on Electronic Imaging Science & Technology Visual Communications and Image Processing, San Jose, CA, Vol. 3653, pp. 1106-1177, 1999.
[30] Yang, J. Wright, T. Huang and Y. Ma, “Image superresolution via sparse representation of raw image patches”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
[31] Mancas-Thillou and M. Majid. ”Super-resolution text using the teager filter.” First International Workshop on Camera-Based Document Analysis and Recognition. pp. 10-16, 2005.