A method and an apparatus of designing a set of wavelet basis trained to fit a particular problem. The method and apparatus include constructing a neural network of arbitrary complexity using a discrete and finite Radon transform, feeding an input wavelet prototype through the neural network and its backpropagation to produce an output, and modifying the input wavelet prototype using the output.
This application claims the benefit of U.S. Provisional Application No. 60/178,061, "METHOD OF CREATING APPLICATION SPECIFIC, NON-UNIFORM WAVELET TRANSFORMS", filed Jan. 24, 2000.
A Radon transform of the image(s) or array(s); and convolution of the Fourier transform of any 1D filter or mask, e.g., wavelet filters, with a 1D Ram-Lak, or other band-limited filter; convolution of the resultant 1D filters with each of the 1D columns of the 2D Radon transform or projection space version of the image; and an inverse Radon transform of the now omnidirectionally filtered projection space version of the image either directly, or after transmission.
A system and method for effectively encoding and decoding electronic information includes an encoding system with a tiling module that initially divides source image data into data tiles. A frame differencing module then outputs only altered data tiles to various processing modules that convert the altered data tiles into corresponding tile components. A quantizer performs a compression procedure upon the tile components to generate compressed data according to an adjustable quantization parameter. An adaptive entropy selector then selects one of a plurality of available entropy encoders to most effectively perform an entropy encoding procedure to thereby produce encoded data. The entropy encoder may also utilize a feedback loop to adjust the quantization parameter in light of current transmission bandwidth characteristics.
