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Abstract

1 Dept of Information Technology, Rajiv Gandhi University (Central University), Arunachal Pradesh, India. 2 Dept of Electronics and Communication Engg , NERIST (Deemed University), Arunachal Pradesh, India.

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Abstract

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  1. 1Dept of Information Technology, Rajiv Gandhi University (Central University), Arunachal Pradesh, India. 2Dept of Electronics and Communication Engg, NERIST (Deemed University), Arunachal Pradesh, India. BPNN and Lifting Wavelet based Image CompressionRenu Singh1, Swanirbhar Majumder2, Utpal Bhattacharjee1, AheibamDinamaniSingh2

  2. Abstract • Compression of data in any form is a large and active field as well as a big business. Image compression is a subset of this huge field of data compression, where the compression of image data is taken specifically. Wavelet transform is one of the popular transforms used in this field and its lifting based variant has become very popular for its easy hardware implementability. For images, the inter-pixel relationship is highly non-linear and unpredictive in the absence of a prior knowledge of the image itself. The back propagation based neural network (BPNN) takes into account the psycho visual features, dependent mostly on the information contained in images. Thereby preserving most of the characteristics of the data while working in a lossy manner and maximize the compression performance. So here image compression based on the lifting wavelet transform is taken in to account along with the BPNN based adaptive technique. Firstly by varying quantization levels for the lifting wavelet transform and number of hidden neurons for the BPNN an optimized compression percentage is reached for suitable adaptive hardware implementation of image compression with both the techniques.

  3. Adaptive h/w compression scheme using optimized BPNN and LDWT

  4. Result Analysis • The LDWT based compression algorithm of Majumder et. al has been used for the variation of PSNR and compression percentage with respect to number of quantization level is plotted for the CDF wavelet, single decomposition. • The algorithm of Dutta et al. has been used with the same for variation of number of hidden neurons has been plotted.

  5. % compression v/s PSNR varying quantization levels for LDWT

  6. % compression v/s PSNR varying hidden neurons for BPNN

  7. Curve fitted data of Compression v/s PSNR (for BPNN and LDWT)

  8. Optimization using Loess quadratic fit smoothing • Here it has been seen that their point of intersection is at 79.2% compression. • This corresponds to 12 hidden neurons for BPNN and 32 quantization levels of LDWT • Loess quadratic fit smoothing of the data and fitting of polynomial curves of order 3 for BPNN and order 4 for LDWT. • Using QLO =32 and HNO=12, for adaptive hardware implementation using a hybrid technique of both BPNN and LDWT can be implemented.

  9. Conclusion • A method of utilizing the advantage of lifting based discrete wavelet transform (LDWT) for hardware implementation and that of BPNN for adaptivity has been used here. • The optimization of the number of quantization levels and number of hidden neurons has been done using curve fitting polynomials and loess quadratic fit. • When implemented on hardware successfully the hybrid variant would be providing a good quality output of PSNR of around 30-35 dB. • Moreover the compression percentage too will be good as it will be providing above 80% compression with higher speed due to hardware implementation.

  10. References • Junejo, N et al, Speech and Image Compression Using Discrete Wavelet Transform, IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication, 2005 • Akintola, A.A. et al, Evaluation of Discrete Cosine Transform (DCT) for Reconstructing Lost Blocks in Wireless Video Transmission, Proceeding ACIT - Signal and Image Processing – 2005 • Andrew, J.P. et al, Modified discrete wavelet transform for odd length data appropriate for image and video compression applications, IEEE, 2001 • Dutta, et. al., "Digital Image Compression using Neural Networks", pg 116-120, in International Conference on Advances in Computing, Control and Telecommunication Technologies'2009(ACT 2009), IEEE Computer Society, Trivendum, Dec’09. ISBN 978-0-7695-3915-7. • R. Claderbank, et. al., “Wavelet Transforms that map integers to integers”, Applied and computational harmonica analysis, 5(3), 332-369, 1998. • A. Shnayderman, et. al., “An SVD-based grayscale image quality measure for local and global assessment”, IEEE Transactions on Image Processing, Vol: 15(2), pp- 422-429. • S. Anna Durai, E. Anna Saro, “Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function”,pp185-189 International Journal for Applied Science and Engg. Technology • S. Majumder et. al., "Image Compression using Lifting Wavelet Transform", proceedings of International Conference on Advances in Communication, Network, and Computing – CNC 2010.

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