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.

Abstract: A method and apparatus for adaptive bit allocation and hybrid lossless entropy encoding in a lossy compression system. The invention includes three components: (1) a transform stage to decorrelate image data into a baseband and multiple subbands, (2) a quantization stage to quantize the resulting transform coefficients, and (3) a lossless entropy coder stage to encode the quantized indexes. In the preferred embodiment, the transform stage uses a wavelet transform algorithm. The quantization stage adaptively estimates values for parameters defining an approximation between quantization size and the logarithm of quantization error, and recursively calculates the optimal quantization size for each band to achieve a desired bit rate. The baseband and subbands are transformed into quantization matrices using the corresponding quantization sizes.