The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding >

http://dns2.asia.edu.tw/~ysho/YSHO-English/2000%20Engineering/PDF/IEE%20Tra%20Sig%20Pro41,%203445.pdf

Embedded image coding using zerotrees of wavelet coefficients

First published in 1995, Wavelets and Subband Coding offered a unified view of the exciting field of wavelets and their discrete-time cousins, filter banks, or subband coding. The book developed the theory in both continuous and discrete time, and presented important applications. During the past decade, it filled a useful need in explaining a new view of signal processing based on flexible time-frequency analysis and its applications. Since 2007, the authors now retain the copyright and allow open access to the book.

Wavelets and Subband Coding

A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided. >

Linear and quadratic time-frequency signal representations

Electronic still imaging apparatus includes a digital signal processor (22) that transforms blocks of digital image signals derived from an image sensor (12) into sets of coefficient signals and encodes the coefficient signals into a stream of compressed signals. In addition, the digital processor generates reduced resolution image signals from the digital image signals and downloads both the compressed (high resolution) image signals and the reduced resolution image signals to a removable digital memory (24). By associating each high resolution image with its low resolution counterpart in a common multi-format image file, the image can be quickly accessed and a low resolution review image can be put up on a display device (116) without waiting for expansion and processing of the larger full resolution image.

Electronic still camera providing multi-format storage of full and reduced resolution images

Multiwavelets are a new addition to the body of wavelet theory. Realizable as matrix-valued filterbanks leading to wavelet bases, multiwavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar two-channel wavelet systems. After reviewing this theory, we examine the use of multiwavelets in a filterbank setting for discrete-time signal and image processing. Multiwavelets differ from scalar wavelet systems in requiring two or more input streams to the multiwavelet filterbank. We describe two methods (repeated row and approximation/deapproximation) for obtaining such a vector input stream from a one-dimensional (1-D) signal. Algorithms for symmetric extension of signals at boundaries are then developed, and naturally integrated with approximation-based preprocessing. We describe an additional algorithm for multiwavelet processing of two-dimensional (2-D) signals, two rows at a time, and develop a new family of multiwavelets (the constrained pairs) that is well-suited to this approach. This suite of novel techniques is then applied to two basic signal processing problems, denoising via wavelet-shrinkage, and data compression. After developing the approach via model problems in one dimension, we apply multiwavelet processing to images, frequently obtaining performance superior to the comparable scalar wavelet transform.

/pdf/the-application-of-multiwavelet-filterbanks-to-image-1g5nj4t750.pdf

The application of multiwavelet filterbanks to image processing

Multilevel unitary wavelet transform methods for image compression are described. The sub-band
decomposition preserves geometric image structure within each sub-band or level. This yields a multilevel
image representation. The use of orthonormal bases of compactly supported wavelets to represent a
discrete signal in 2 dimensions yields a localized representation of coefficient energy. Subsequent coding
of the multiresolution representation is achieved through techniques such as scalar/vector quantization,
hierarchical quantization, entropy coding, and non-linear prediction to achieve compression.
Performance advantages over the Discrete Cosine Transform are discussed. These include reduction of
errors and artifacts typical of Fourier-based spectral methods, such as frequency-domain quantization
noise and the Gibbs phenomenon. The wavelet method also eliminates distortion arising from data
blocking. The paper includes a quick review of past/present compression techniques, with special
attention paid to the Haar transfOrm, the simplest wavelet transform, and conventional Fourier-based subband
coding. Computational results are presented.

Application of compactly supported wavelets to image compression

A system for compressing images is disclosed. The system utilizes a transformation which is equivalent to expanding the image using a system of wavelets having finite support.

Image compression method and apparatus

A method and apparatus for compressing motion pictures is disclosed. The frames of the motion picture are divided into adjacent groups. Each group is treated as a three-dimensional image. The three-dimensional image is then filtered via a three-dimensional FIR filter to generate three-dimensional component images that are more efficiently quantized. The degree of quantization of each component image is determined in part by the spatial frequencies represented by the component image in question. For motion pictures derrived from interlaced scanning devices, the quantization of specific component images is altered to prevent artifacts.

Method and apparatus for coding motion pictures

A system (10) compressing images is disclosed. The system utilizes a transformation (12) which is equivalent to expanding the image using a system of wavelets having finite support.

Improved image compression method and apparatus

http://ieeexplore.ieee.org/iel7/7227860/7240418/07240423.pdf

John C. Huffman

Papers

Application of compactly supported wavelets to image compression

Image compression method and apparatus

Method and apparatus for coding motion pictures

Improved image compression method and apparatus

SMPTE Tutorial: Wavelets and Image Compression