Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model(ISRCS) is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

[1]  Wilfried Philips Weakly separable bases for fast segmented image coding , 1994, Defense, Security, and Sensing.

[2]  Ma Yi-de,et al.  A new kind of impulse noise filter based on PCNN , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[3]  Andy C. Yu,et al.  Compact representation of contours using directional grid chain code , 2008, Signal Process. Image Commun..

[4]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Wilfried Philips,et al.  Fast segmented image coding using weakly separable bases , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Yide Ma,et al.  A region segmentation method for region-oriented image compression , 2012, Neurocomputing.

[7]  Murat Kunt,et al.  Recent results in high-compression image coding (Invited Papaer) , 1987 .

[8]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  M. Gilge Region-oriented transform coding (ROTC) of images , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[10]  M. Kunt,et al.  Second-generation image-coding techniques , 1985, Proceedings of the IEEE.

[11]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[12]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[13]  Li Guangyao,et al.  Geometric active contours without re-initialization for image segmentation , 2009 .

[14]  O. J. Morris,et al.  Segmented-image coding: Performance comparison with the discrete cosine transform , 1988 .

[15]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[16]  Charles T. Zahn,et al.  and Describing GestaltClusters , 1971 .

[17]  Borut Zalik,et al.  An efficient chain code with Huffman coding , 2005, Pattern Recognit..

[18]  Thomas Engelhardt,et al.  Coding of arbitrarily shaped image segments based on a generalized orthogonal transform , 1989, Signal Process. Image Commun..

[19]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Zhang Hong-juan A New Method of Color Image Enhancement Using Spiking Cortical Model , 2012 .

[21]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[22]  R. Langer Interpolation and Approximation by Rational Functions in the Complex Domain , 1937 .

[23]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[24]  Wilfried Philips,et al.  FAST CODING OF ARBITRARILY SHAPED IMAGE SEGMENTS USING WEAKLY SEPARABLE BASES , 1996 .

[25]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[26]  Norman D. Black,et al.  Second-generation image coding: an overview , 1997, CSUR.

[27]  Yide Ma,et al.  New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing , 2009, IEEE Transactions on Neural Networks.

[28]  Yide Ma,et al.  Applications of Pulse-Coupled Neural Networks , 2011 .

[29]  Reinhard Eckhorn,et al.  Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[30]  Roderick Urquhart,et al.  Graph theoretical clustering based on limited neighbourhood sets , 1982, Pattern Recognit..

[31]  Luisa Verdoliva,et al.  Region-Based Transform Coding of Multispectral Images , 2007, IEEE Transactions on Image Processing.

[32]  S. Beucher,et al.  Watersheds of functions and picture segmentation , 1982, ICASSP.