Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
A two-component model of texture for analysis and synthesis
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Local affine image matching and synthesis based on structural patterns
IEEE Transactions on Image Processing
An affine symmetric image model and its applications
IEEE Transactions on Image Processing
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We approach image compression using an affine symmetric image representation that exploits rotation and scaling as well as the translational redundancy present between image blocks. It resembles fractal theory in the sense that a single prototypical block is needed to represent other similar blocks. Finding the optimal prototypes is not a trivial task particularly for a natural image. We propose an efficient technique utilizing independent component analysis that results in near-optimal prototypical blocks. A reliable affine model estimation method based on Gaussian mixture models and modified expectation maximization is presented. For completeness, a parameter entropy coding strategy is suggested that achieves as low as 0.14 bpp. This study provides an interesting approach to image compression although the reconstruction quality is slightly below that of some other methods. However the high frequency details are well-preserved at low bitrates, making the technique potentially useful in low bandwidth mobile applications.