Image compression by linear splines over adaptive triangulations

This paper proposes a new method for image compression. The method is based on the approximation of an image, regarded as a function, by a linear spline over an adapted triangulation, D(Y), which is the Delaunay triangulation of a small set Y of significant pixels. The linear spline minimizes the distance to the image, measured by the mean square error, among all linear splines over D(Y). The significant pixels in Y are selected by an adaptive thinning algorithm, which recursively removes less significant pixels in a greedy way, using a sophisticated criterion for measuring the significance of a pixel. The proposed compression method combines the approximation scheme with a customized scattered data coding scheme. We compare our compression method with JPEG2000 on two geometric images and on three popular test cases of real images.

[1]  Armin Iske,et al.  Multiresolution Methods in Scattered Data Modelling , 2004, Lecture Notes in Computational Science and Engineering.

[2]  A. Cohen Multivariate Approximation and Applications: Applied and computational aspects of nonlinear wavelet approximation , 2001 .

[3]  D. Donoho Wedgelets: nearly minimax estimation of edges , 1999 .

[4]  Stéphane Mallat,et al.  Sparse geometric image representations with bandelets , 2005, IEEE Transactions on Image Processing.

[5]  Nira Dyn,et al.  Low Bit-Rate Image Coding Using Adaptive Geometric Piecewise Polynomial Approximation , 2007, IEEE Transactions on Image Processing.

[6]  Olivier Devillers,et al.  Geometric compression for interactive transmission , 2000 .

[7]  Neil A. Dodgson,et al.  Advances in Multiresolution for Geometric Modelling , 2005 .

[8]  Albert Cohen,et al.  Compact representation of images by edge adapted multiscale transforms , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Justin K. Romberg,et al.  Multiscale wedgelet image analysis: fast decompositions and modeling , 2002, Proceedings. International Conference on Image Processing.

[11]  Emmanuel J. Candès,et al.  Curvelets and Curvilinear Integrals , 2001, J. Approx. Theory.

[12]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[13]  Jerome M. Shapiro,et al.  An embedded hierarchical image coder using zerotrees of wavelet coefficients , 1993, [Proceedings] DCC `93: Data Compression Conference.

[14]  R. DeVore,et al.  Nonlinear approximation , 1998, Acta Numerica.

[15]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[16]  N. Dyn,et al.  Multivariate Approximation and Applications: Index , 2001 .

[17]  Venkat Chandrasekaran,et al.  Compression of Higher Dimensional Functions Containing Smooth Discontinuities , 2004 .

[18]  Armin Iske,et al.  Scattered Data Coding in Digital Image Compression , 2002 .

[19]  Michael T. Orchard,et al.  Wavelet packet image coding using space-frequency quantization , 1998, IEEE Trans. Image Process..

[20]  A. Iske,et al.  Advances in Digital Image Compression by Adaptive Thinning , 2003 .

[21]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[22]  David S. Taubman,et al.  High performance scalable image compression with EBCOT , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[23]  Aria Nosratinia,et al.  Wavelet-Based Image Coding: An Overview , 1999 .

[24]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[25]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[26]  Yehoshua Y. Zeevi,et al.  The farthest point strategy for progressive image sampling , 1997, IEEE Trans. Image Process..

[27]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[28]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[29]  Richard Baraniuk,et al.  Multiscale Approximation of Piecewise Smooth Two-Dimensional Functions using Normal Triangulated Meshes , 2005 .

[30]  N. Dyn,et al.  Adaptive thinning for bivariate scattered data , 2002 .

[31]  Nira Dyn,et al.  Adaptive Thinning for Terrain Modelling and Image Compression , 2005, Advances in Multiresolution for Geometric Modelling.

[32]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[33]  Larry L. Schumaker,et al.  Curve and Surface Fitting: Saint-Malo 1999 , 2000 .