Multi Directional Wavelet Filter Based Region of Interest Compression for Low Resolution Images
An increase in the demand for storing the large archrivals’ of medical image data bases and the image data base for surveillance applications paved way for Region of Interest (ROI) based image compression techniques. Different ROI based coding techniques identify fixed shaped regions for compression. However, the real world images are having irregular shaped ROI. So, extraction of directional information along with identification of relevant information along multiple resolutions is very important for performing ROI based compression. Therefore, in this paper a Multi Directional Wavelet Based ROI Compression for low resolution images is proposed. Furthermore, the ROI is encoded using lossless encoding techniques for obtaining good resolution and the rest of the image is coded with lossy image compression techniques for obtaining high compression ratio. The proposed algorithm is compared with JPEG2000 standard which uses ROI for compression of images, with arithmetic encoder which is a lossless image compression method and also with SPIHT encoder which is a lossy image compression method.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
J.L. Starck, F. Murtagh, A. Bijarcu, “Image Processing and Data Analysis”, Cambridge University Press,2004.
Sanchez, V.; Basu, A.; Mandal, M.K., “Prioritized region of interestcoding in JPEG2000”, IEEE Trans. on CSVT, 14(9) 2004
M. W. Marcellin, JPEG2000 Image Compression Fundamentals, Standards and Practice, Springer 2002
G Liu, X Zeng, F Tian, K Chaibou, Z Zheng“A novel direction-adaptive wavelet based image compression, AEU - International Journal of Electronics and Communications, Volume 64, Issue 6, June 2010, Pages 531–539
James E. Fowler, Sungkwang Mun, Eric W. Tramel Block-Based Compressed Sensing of Images and Video, Foundations and Trends in Signal Processing , Volume 4 Issue 4, Now Publishers Inc. Hanover, MA, USA
Liu Yu, King Ngi Nganm Feng Wu, “3-D Shape Adaptive Directional Wavelet Transform for Object Based Scalable Video Coding”, IEEE Transactions on Circuits and Systems, Vol:18, Issue:7, 2008,pp:888-899
Shipeng Li, Weiping Li Shape-Adaptive Discrete Wavelet Transforms for Arbitrarily shaped Visual Object Coding”, IEEE Transactions on Circuits and Systems for Video Technology, Volume10, Issue:5, 2000 ,pp:725-743
Liu Yu, King Ngi Nganm Feng Wu, “3-D Object Based Scalable Wavelet Video Coding with Boundary Effect Suppression”, IEEE Transactions on Circuits and Systems for Video Technology, Vol:17, Issue:5, 2007,pp:639-644
Amir Said, William. A. Pearlman, “ A new fast and efficient image coder based on Set Partitioning in Hierarchial Trees”, IEEE trans CAS for Video technology, vol.6,June 1996
S. Mallat “A wavelet tour of Signal processing”, 2nd edition Academic press, 1999.
H. Bamberger and M. J. T Smith Jeng-Shyang Pan and Jing-Wein Wang, “Texture Segmentation Using Separable and Non-separable Wavelet Frames”, IEICE Transaction Fundamental, Vol. E82-A, No. 8, pp. 1463-1474, Aug., 1999.
Dong Liu, Xiayon Sun, Feng Wu, Ya Qin Zhang, “Edge oriented Uniform Intra Prediction” IEEE trans on Image Processing vol 17, No 10 , Oct 2008.
Anton Brezina, JaraslavPolec, “Region Based Texture Coding at Very Low Bit Rates”, Journal of Electrical Engineering, Vol. 56, No1-2, 2005 pp 36-40.
Hannes Hartenstein et al, “Region Based Fractal Image Compression”, IEEE Transactions on Image Processing, Vol.9, No.7, July 2000.
Phan T.H. Truc, Md. A.U. Khan, Young-Koo Lee, “Vessel Enhancement Filter Using Directional Filter Bank” Computer Vision and Image Understanding, Vol 113, issue 1, pp 101-112, 2008
Hari, V.S., Jagathy Raj, V.P., Gopikakumari, R., Spatial filtering of MRI images for the removal of impulsive noise using quadratic Volterra filter, (2012) International Journal on Communications Antenna and Propagation (IRECAP), 2 (4), pp. 252-258.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2020 Praise Worthy Prize