Retinal-inspired filtering for dynamic image coding

This paper introduces a novel non-Separable sPAtioteMporal filter (non-SPAM) which enables the spatiotemporal decomposition of a still-image. The construction of this filter is inspired by the model of the retina which is able to selectively transmit information to the brain. The non-SPAM filter mimics the retinal-way to extract necessary information for a dynamic encoding/decoding system. We applied the non-SPAM filter on a still image which is flashed for a long time. We prove that the non-SPAM filter decomposes the still image over a set of time-varying difference of Gaussians, which form a frame. We simulate the analysis and synthesis system based on this frame. This system results in a progressive reconstruction of the input image. Both the theoretical and numerical results show that the quality of the reconstruction improves while the time increases.

[1]  Seong-Dae Kim,et al.  Spatio-temporal video segmentation using a joint similarity measure , 1997, IEEE Trans. Circuits Syst. Video Technol..

[2]  G. DeAngelis,et al.  Spatiotemporal receptive field organization in the lateral geniculate nucleus of cats and kittens. , 1997, Journal of neurophysiology.

[3]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[4]  Siddhartha Choubey,et al.  Discrete Wavelet Transform for Image Processing , 2014 .

[5]  Pierre Kornprobst,et al.  Frames for Exact Inversion of the Rank Order Coder , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[7]  Mark J. T. Smith,et al.  Spatio-temporal wavelet transforms for motion tracking , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[9]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[10]  Pierre Kornprobst,et al.  Streaming an image through the eye: The retina seen as a dithered scalable image coder , 2012, Signal Process. Image Commun..

[11]  Detlev Marpe,et al.  Performance comparison of H.265/MPEG-HEVC, VP9, and H.264/MPEG-AVC encoders , 2013, 2013 Picture Coding Symposium (PCS).

[12]  Xianguo Zhang,et al.  The IEEE 1857 Standard: Empowering Smart Video Surveillance Systems , 2014, IEEE Intelligent Systems.

[13]  Tim Gollisch,et al.  Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina , 2010, Neuron.

[14]  Steffen Beich,et al.  Digital Video And Hdtv Algorithms And Interfaces , 2016 .

[15]  Iain E. G. Richardson,et al.  The H.264 Advanced Video Compression Standard , 2010 .

[16]  Jelena Kovacevic,et al.  An Introduction to Frames , 2008, Found. Trends Signal Process..

[17]  Pierre Kornprobst,et al.  Virtual Retina: A biological retina model and simulator, with contrast gain control , 2009, Journal of Computational Neuroscience.

[18]  Edward H. Adelson,et al.  Orthogonal Pyramid Transforms For Image Coding. , 1987, Other Conferences.

[19]  Rufin van Rullen,et al.  Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex , 2001, Neural Computation.

[20]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[21]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[22]  Andrew B. Watson,et al.  The cortex transform: rapid computation of simulated neural images , 1987 .

[23]  R. Masland.  The fundamental plan of the retina , 2001, Nature Neuroscience.

[24]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[25]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.