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Joshi, M. A.
- Neuro-wavelet Based Vector Quantizer Design for Image Compression
Authors
1 Vishwakarma Institute of Information Technology , Pune, IN
2 Govt. College of Engg., Pune, IN
Source
Indian Journal of Science and Technology, Vol 2, No 10 (2009), Pagination: 56-61Abstract
This paper presents a novel idea of designing codebook, which is heart of vector quantization scheme based on Kohonens self organizing feature maps (SOFM) and wavelet transform. Being named as neuro wavelet generic codebook for compression of gray images, it can also be extended to the compression of color images and video frames. The code vectors are generated by evaluating the characteristics of the specific image sub samples, which are determined through rigorous mathematical operations and training the selected samples by Kohonen's SOFM artificial neural network with adjustable learning rate and initializations conditions followed by application of discrete wavelet transform. The testing of the codebook is done with variety of images and the compression performance is evaluated by using objective and subjective quality measures such as image fidelity, structural content, mean structural similarity index, universal quality index, spatial frequency measure and spectral activity measure along with PSNR. The proposed design is such that it can be implemented with less complexity and cost using VLSI techniques.Keywords
Image Compression, Neuro-wavelet, VectorReferences
- Ahmet M. Eskicioglu and Paul S. Fisher (1995) Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959-2965.
- Amerijckx C, Michel Verleysen, Philippe Thissen, and Jean-Didier Legat (1998) Image compression by selforganized Kohonen map. IEEE Trans. Neural Networks. 9 (3), 503-509.
- Amerjickx C(2003) Image compression using self organizing maps. J. System Analysis Modeling Simulation. 43(11) 1529-1547.
- Anna Durai and Anna Rao E (2006) An improved image compression approach with self organizing map using cumulative distribution function. GVIP J. 6 (2), 41-50.
- Dandawate YH and Joshi MA (2007) Performance analysis of image compression using enhanced vector quantizer designed with self organizing feature maps. Proc. IEEE Intl. Conf. on Comput. Intelligence & Multimedia Appln. (ICCIMA 07) Sivkashi, Tamilnadu. 3,128-132.
- Dandawate YH, Joshi MA and Umrani SM (2008) Performance comparision of color image compression based on enhanced vector quantizers designed using different color spaces. Proc. IEEE Intl. Conf. on Intelligent & Adv. Systems (ICIAS 07) Kuala Lumpur Malaysia. (1) 626-630.
- Dianhui Xu, Robert Li and David Song (2008) A new approach to image compression using vector quantization of wavelet coefficients. IEEE Int. Conf. Neural Networks & Signal Processing. Zhenjiang, China. pp: 95-99.
- Gersho A and Gray RM (1992) Vector Quantization and signal compression. Norwell, MA, Kluwer.
- Gray RM (1984) Vector quantization. IEEE ASSP Magazine. 1, 4–29.
- Ismail Avicibas, Bulent Sankur and Khalid Sayood (2002) Statistical evaluation of image quality measures. J. Electronic Imaging. 11(2), 206-223.
- Jayanta Kumar Debnath, Newaz Muhammad Syfur Rahim and Wai-keung Fung (2008) A modified vector quantization based image compression technique using wavelet transform. Proc. IEEE Intl. Joint Conf. on Neural Networks. HongKong 1(8), 171-176.
- Kohonen T (1988) Self-Organization and associative memory. 2nd ed., New York, Springer-Verlag,
- Kohonen T (1990) The self organizing maps. In: Invited Paper, Proc. of IEEE, 78 (9), 1464-1480.
- Laha A, Pal NR and Chanda B (2004) Design of vector quantizer for image compression using self organizing feature map and surface fitting. IEEE Trans. Image Processing. 13. 1291-1302.
- Lawson S and Zhu J (2002) Image compression using wavelets and JPEG2000: a tutorial. Electrn. Commun. Engg. J. 5(1) 113-123.
- Linde A Buzo and Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans. Commun. 28 (1), 84–95.
- Marta Mrak, Sonja Grgic and Mislav Grgic (2003) Picture quality measures in image compression systems. EUROCON, Ljuijana, Slovenia.
- Nasrabadi NM and King RA (1988) Image coding using vector quantization: a review. IEEE Trans. Commun. 36 (8), 957–971.
- Nasrabadi NM and Yushu Feng (1990) Image compression using address vector quantization. IEEE Trans. Commun. 38 (2), 2166-2173.
- Robert Dony and Simon Haykin (1995) Neural network approaches to image compression. Proc. of IEEE. 83 (2), 288-303.
- Robert M Gray and David L Neuhoff (1998) Quantization. IEEE Trans. Info. Theory. 44 (6), 1-63
- Ronald A DeVore, Bjorn Jawerth and Bradley J Lucier (1992) Image compression through wavelet transform coding. IEEE Trans. Info. Theory. 38 (2), 719-746.
- Simon Haykin (2001) Neural networks: a comprehensive foundation. 2nd ed., Pearson Education. India
- Sudhakar R, Karthiga R and Jayaraman S (2006) Image compression using coding of wavelet coefficients– a survey. GVIP J. 6 (1), 1-14.
- Teuvo Kohonen (2001) Self-Organizing maps. 3rd extended ed., Springer, Berlin, Heidelberg.
- Vasudev Bhaskaran and Konstantinos Konstantinides (1997) Image and video compression standards: algorithms and architectures. In: The Springer Intl. Sr. in Engg. & Compu. Sci. Berlin.
- Zhou Wang, Alan Conrad Bovik,, Hamid Rahim Sheikh, and Eero P. Simoncelli (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 13(4), 600-613.
- Vector Algebra Based Tracing of External and Internal Boundary of an Object in Binary Images
Authors
1 International Institute of Information Technology, P-14 Rajiv Gandhi Infotech Park, Hinjewadi, Pune-411057, IN
2 Collage of Engineering, Wellesely Rd, Shivajinagar, Pune, -411005, IN
Source
Journal of Advances in Engineering Sciences, Vol 3, No 1 (2010), Pagination: 57-70Abstract
The present work details an algorithm using vector algebra for tracing a boundary of a binary pattern. More specifically, the paper provides a theoretical framework for boundary tracing and provides a basis for boundary tracing task. Core concept in the present work is the idea of seeking minimum angle between a vector formed by present and previous boundary locations, and the vectors formed by present and next possible boundary locations, using vector algebra. The proposed algorithm uses both cross and dot product for the choice of the minimum angle. This algorithm overcomes the drawbacks of heuristic approaches and provides a complete solution to the tracing problem. These include solutions taking into account a variety of contour possibilities external or internal on one hand and open or closed on the other, and the combinations thereof.
Use of sound mathematical basis in this tracing algorithm can avoid the drawbacks inherent in heuristic approaches. The paper includes proof for this algorithm providing 100% sensitivity and completeness. The trial runs performed on more than 1300 test images yielded a set of ordered boundary pixels 100% of the time.