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Performance Analysis of Daubechies Wavelet and Differential Pulse Code Modulation Based Multiple Neural Networks Approach for Accurate Compression of Images
Siripurapu Sridhar, P.Rajesh Kumar, K.V.Ramanaiah
Pages - 372 - 384     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 4    |    Publication Date - September 2013  Table of Contents
Backpropagation, Daubechies Wavelet, DPCM, PSNR, MSE, Neural Networks.
Large Images in general contain huge quantity of data demanding the invention of highly efficient hybrid methods of image compression systems involving various hybrid techniques. We proposed and implemented a Daubechies wavelet transform and Differential Pulse Code Modulation (DPCM) based multiple neural network hybrid model for image encoding and decoding operations combining the advantages of wavelets, neural networks and DPCM because, wavelet transforms are set of mathematical functions that established their viability in the areas of image compression owing to the computational simplicity involved in their implementation, Artificial neural networks can generalize inputs even on untrained data owing to their massive parallel architectures and Differential Pulse Code Modulation reduces redundancy based on the predicted sample values. Initially the input image is subjected to two level decomposition using Daubechies family wavelet filters generating high-scale low frequency approximation coefficients A2 and high frequency detail coefficients H2, V2, D2, H1, V1 and, D1 of multiple resolutions resembling different frequency bands. Scalar quantization and Huffman encoding schemes are used for compressing different sub bands based on their statistical properties i.e the low frequency band approximation coefficients are compressed by the DPCM while the high frequency band coefficients are compressed with neural networks. Empirical analysis and objective fidelity metrics calculation is performed and tabulated for analysis.
CITED BY (1)  
1 Ramanaiah, K. V., & Sridhar, S. Soft Computing Artificial Neural Networks and Transform Based Image Compression Techniques-An Analysis.
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Amjan Shaik and Dr.C.K.Reddy,”Empirical Analysis of Image Compression through wave transform and Neural Network”, International Journal of Computer Science and Information Technologies (IJCSIT), vol.2 (2), 2011, 924-931.
Aran Namphol, Steven H.Chin and Mohammed Arozullah, “Image Compression with a Hierarchial Neural Network”, IEEE Teransactions on Aerospace and Electronic Systems vol 32, no 1 January1996.
B.Eswara Reddy and K.Venkata Narayana, “A lossless image compression using traditional and lifting based wavelets”
Christopher J.C.Burges, Ptrice Y.Simrad ,” Improving Wavelet image compression with Neural Networks:
Chun-Lin, Liu, “ A tutorial of the Wavelet Transform”.
Faisal Zubir Quereshi, “Image Compression using Wavelet Transform”.
G.L.Sicuranzi, G.Ramponi and S.Marsi, “Artificial Neural Network for Image Compression”,Electronic Letters, vol26, no.7,pp. 477-479, March 29 1990.
Hahn-Ming Lee, Tzong-Ching Huang and Chih-Ming Chen, “Learning Efficiency Improvement of Backpropagation Algorithm by Error Saturation Prevention Method, 0-7803-5529-6/992@1999 IEEE.
Jose Prades Nebot, Edward J.Delp,” Genaralized PCM coding of images” IEEE transactions on image processing , VOL 21,N o 8, August 2012
K.Siva Nagi Reddy, Dr.B.R.Vikram,, B.Sudheer Reddy and L.Koteswararao, “Image Compression and Reconstruction using a new approach by Artificial Neural Network”,International Journal of Image Processing (IJIP), Volume (6): Issue (2):2012.
Kareen Lees, “Image compression using wavelets”.
Liu-Yue Wang and EARKKI Oja, “Image Compression by Neural Networks: A comparison study”.
Marta Mrak and Sonia Grgic, “Picture quality Measures in Image Compression Systems”,EUROCON 2003 Ljubljana, Slovenia.
Mohammed A. Salem, Nivin Ghamry, and Beate Meffert, “Daubechies versus Biorthogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems”.
Petros T BouFounos, “ Universal rate efficient scalar quantization” IEEE transactions on information theory ,VOL 58, No 3, March 2012
Priyanka Singh, Priti Singh,” JPEG Image Compression based on Biorthogonal, coiflets and Daubechies Wavelets”.
Ranbeer Tyagi, ” Image Compression using DPCM with LMS algorithm” an international society of thesis publications.
S.Anna Durai and E.Anna Saro, “Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function”, World Academy of Science Engineering and Technology 17, 2006.
S.Sridhar, P.Rajesh Kumar and K.V.Ramanaiah, “ An efficient hybrid image coding scheme combining neural networks, wavelets and DPCM for image compression” International Journal of Computer Applications.
Sonal and Dinesh Kumar, “A study of various Image Compression Techniques”, Guru Jhmbheswar university of science and technology, Hisar.
Yogendra Kumar Jain and Sanjeev Jain, “Performance Evaluation of Wavelets for Image Compression”.
Mr. Siripurapu Sridhar
Dr. P.Rajesh Kumar
Dr. K.V.Ramanaiah