The Aerospace Corporation

Xerox Corporation

University Microfilms, Inc.

Hsieh S Hou

Hosni Adra

John E Brooks

Donald F Willis

A shared lossless Haar transform and an appended Haar transform are combined to form a lossless extended Haar transform in a pipeline architecture for providing fast lossless compressed data that is reversible. The extended Haar transform also provide intrinsic decorrelation for decorrelating corrected randomly generated numbers.

Extended Haar transform

A system provides lossless split and merge processes of integer discrete cosine transform (DCT) transformed data such that the discrete cosine transform of one data block may be split into two half length DCT odd and even blocks for merging, with split and merge processes being lossless and are generated in the discrete cosine transformed domain. After splitting, the redundancy existing between the two integer discrete cosine transformed half data blocks allows one to approximately reconstruct the original data block in case one of the discrete cosine transformed half data block is lost during transmission. The system may be used with existing JPEG and MPEG compressors and decompressors because both use the discrete cosine transform for image and video compression and decompression, may be used as a resolution conversion device for transcribing from digital high-definition TV to analog low-definition TV, and may be used for lossless splitting and merging type-IV discrete cosine transformed data for audio compression and decompression in the international standard MPEG-4 Advanced Audio Coding (AAC), such as AC-3 or MP3.

Compressed data multiple description transmission and resolution conversion system

A front-end shared Haar transform is particularly adapted for cascade connection to an appended transform for providing lossless transform of an input data set, the appended transforms selected from the group consisting of an appended discrete cosine type-II (DCT-II) transform for forming a lossless DCT-II transform, an appended discrete cosine type-IV (DCT-IV) transform for forming a lossless DCT-IV transform, and an appended Haar transform for forming a lossless extended Haar transform.

Shared Haar wavelet transform

A shared lossless Haar transform and an appended type-IV discrete cosine transform are combined to form a lossless discrete cosine type-IV transform having a fast pipeline architecture for providing fast reversible lossless DCT-IV transform data.

Haar wavelet transform embedded lossless type IV discrete cosine transform

A shared lossless Haar transform and an appended discrete cosine transform type-II are combined to form a discrete cosine type-II transform in a parallel pipelined architecture for providing lossless data transformation.

Haar wavelet transform embedded lossless type II discrete cosine transform

An adaptive lifting transform provides lossless image data compression on an improved edge prediction process. The adaptive predictor works on pixel-by-pixel basis that does not need bookkeeping overhead, especially for integer-to-integer wavelet transform for reducing the number of lifting steps for use in JPEG2000 image compression.

Fast adaptive lifting lossless wavelet transform

The lifting transform reduces the number of lifting steps in lossless data compression while improving the overall rounding errors incurred in the real-to-integer conversion process at each of the lifting steps. The compression ratio increases when used for lossless image data compression. The lifting transform further saves memory space and decreases signal delay.

Fast lifting lossless wavelet transform

The integrated lifting transform provides both the lossy and lossless lifting wavelet transforms while sharing the same lifting chain for either lossy data compression or lossless data compression. The lifting steps can provide a lossless compression and a lossy compression directly from lossless compression while the integer-to-integer adaptive four-stage lifting wavelet transform provides the lossless compression with improved performance.

Integrated lifting wavelet transform

Discrete Cosine Transforms in a radix-2 block transform method enables true split and merge transform processing of equal sized data halves in the transform domain for enabling recursive merges and splits in transform domain without data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward transform processing is firstly transform processed followed by combinational processing. In the split transform process, whole transformed data is split using combinational processing into first and second transformed data halves in the transform domain as a true split. In the merge transform process, first and second transform halves are merged using combinational processing into a merged transformed data whole in the transform domain. In either case, time or spatial domain input data can be transformed into the transform domain in the form of split halves or merged whole. The split halves can be merged by the merge process combinational processing and the merged whole can be split by the split process combinational processing. The split and merge process combinational processing enable recursive splits and merges in the transform domain.

Merge and split discrete cosine block transform method

Fast Fourier Transforms in a radix-2 block transform method enables true split and merge transform processing of equal sized data halves in the Fourier transform domain for enabling recursive merges and splits in Fourier transform domain without data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward transform processing is firstly transform processed followed by combinational processing. Whole transformed data is split using combinational processing into transformed data halves in the transform domain as a true split. Transform halves are merged using combinational processing into whole merged transformed data in the transform domain. Time or spatial domain input data can be transformed into the transform domain in the form of split halves or merged whole. The split halves can be merged by the merge process combinational processing and the merged whole are split by the split process combinational processing.

Merge and split fast fourier block transform method

A generalized radix-2 block transform method enables true split and merge transform processing of equal sized data halves in the transform domain enabling recursive merges and splits in transform domain data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward transform processing is firstly transform processed followed by combinational processing. Whole transformed data is split using combinational processing into transformed data halves in the transform domain as a true split. The transformed halves are merged using combinational processing into whole merged transformed data in the transform domain. Time or spatial domain input data can be transformed into the transform domain in the form of split halves or merged whole. The split halves can be merged by the merge process combinational processing and the merged whole can be split by the split process combinational processing.

Merge and split generalized block transform method

Fast Hartley Transforms in a radix-2 block transform method enables true split and merge transform processing of equal sized data halves for enabling recursive merges and splits in Hartley transform domain without data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward Hartley transform processing is firstly transform processed followed by combinational processing. Whole transformed data is split using combinational processing into transformed data halves in the Hartley transform domain as a true split. The transformed halves are merged using combinational processing into whole merged transformed data in the Hartley transform domain. Time or spatial domain input data can be transformed into the Hartley transform domain in the form of split halves or merged whole. The split halves can be merged by the merge process combinational processing and the merged whole can be split by the split process combinational processing.

Merge and split fast hartley block transform method

Discrete Karhunen-Loeve Transforms in a radix-2 block transform method enables true split and merge transform processing of equal sized data halves in the KLT domain for enabling recursive merges and splits in the KLT domain without data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward transform processing is firstly transform processed followed by combinational processing. Whole transformed data is split using combinational processing into transformed halves in the transform domain as a true split. Transformed halves are merged using combinational processing into whole merged transformed data in the transform domain. Time or spatial domain input data can be transformed into the transform domain in the form of split halves or merged whole. The split halves are merged by the merge process combinational processing and the merged whole are split by the split process combinational processing.

Merge and split Karhunen-Loeve block transform method

Discrete Sine Transforms in a radix-2 block transform method enables true split and merge transform processing of equal sized data halves in the DST transform domain for enabling recursive merges and splits in DST transform domain without data degradation. Input data in the time domain or spatial domain during either the split and merge radix-2 forward transform processing is firstly transform processed followed by combinational processing. Whole transformed data is split using combinational processing into transformed data halves in the transform domain as a true split. The transformed halves are merged using combinational processing into whole merged transformed data in the transform domain. Time or spatial domain input data can be transformed into the transform domain in the form of split halves or merged whole. The split halves are merged by the merge process combinational processing and the merged whole are split by the split process combinational processing.

Merge and split discrete sine block transform method

A fast modulated lapped transform (MLT) method and architecture for image compression and decompression systems minimizes blocking artifacts associated with JPEG based discrete cosine transform (DCT) image compression systems. The MLT method combines fast block processing capabilities of wavelet transforms and fast block processing of DCT image compression systems. The modular and pipeline MLT architecture is fast by block processing but avoids the visual blocking artifacts that can be seen in most DCT-based compression systems. Improved MLT processors are implemented by an infinite impulse response filter operating on a product of the MLT window function and the input data stream. Forward and reverse MLT processors include a new fused multiply-add logic for fast computations and localized interconnections. The MLT processors can be combined into a bank of parallel processors in a one dimensional MLT architecture, which can be used for two-dimensional image transformation. The improved MLT implementation enables a modular architecture having a reduce number of multipliers and interconnects well suited for practical VLSI implementation.

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Rancho Palos Verdes, CA, United States of America

Hsieh S Hou