Abstract: A system and method is disclosed that compresses and decompresses images. The compression system and method includes an encoder which compresses images and stores such compressed images in a unique file format, and a decoder which decompresses images. The encoder optimizes the encoding process to accommodate different image types with fuzzy logic methods that automatically analyze and decompose a source image, classify its components, select the optimal compression method for each component, and determine the optimal parameters of the selected compression methods. The encoding methods include: a Reed Spline Filter, a discrete cosine transform, a differential pulse code modulator, an enhancement analyzer, an adaptive vector quantizer and a channel encoder to generate a plurality of data segments that contain the compressed image. The plurality of data segments are layered in the compressed file to optimize the decoding process.

Abstract: A computationally fast, effective method for compressing digital images uses an optimized subsampling process. A set of spatially overlapping spline functions is used as a basis onto which the image data are projected by use of a least-mean-squares criterion. The specified processes can be implemented for compressing and interpolating digital data arrays of N dimensions. Linear, planar and hyperplanar spline functions allow convenient, fast and efficient closed-form optimal compression, which process is easily incorporated into existing digital processing systems. A key advantage of the disclosed method is the fast coding/reconstruction speed, because it involves only FFT or convolution types of processors.