Browsing by Author "Sweldens, Wim"

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    Adaptive Wavelet Transforms for Image Coding
    (1997-11-01) Claypoole, Roger L.; Davis, Geoffrey; Sweldens, Wim; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We introduce a new adaptive transform for wavelet-based image coding. The lifting framework for wavelet construction motivates our analysis and provides new insight into the problem. Since the adaptive transform is non-linear, we examine the central issues of invertibility, stability, and artifacts in its construction. We describe a new type of non-linearity: a set of linear predictors are chosen adaptively using a non-linear selection function. We also describe how earlier families of non-linear filter banks can be extended through the use of prediction functions operating on a causal neighborhood. We present preliminary results for a synthetic test image.
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    Adaptive Wavelet Transforms for Image Coding
    (1997-11-01) Claypoole, Roger L.; Davis, Geoffrey; Sweldens, Wim; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We introduce a new adaptive transform for wavelet-based image coding. The lifting framework for wavelet construction motivates our analysis and provides new insight into the problem. Since the adaptive transform is non-linear, we examine the central issues of invertibility, stability, and artifacts in its construction. We describe a new type of non-linearity: a set of linear predictors are chosen adaptively using a non-linear selection function. We also describe how earlier families of non-linear filter banks can be extended through the use of prediction functions operating on a causal neighborhood. We present preliminary results for a synthetic test image.
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    Adaptive Wavelet Transforms for Image Coding using Lifting
    (1998-03-01) Claypoole, Roger L.; Davis, Geoffrey; Sweldens, Wim; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    Summary form only given. Image compression relies on efficient representations of images, and within smooth image regions, the wavelet transform provides such a representation. However, near edges, wavelet coefficients decay slowly and are expensive to code. We focus on improving the transform by incorporating adaptivity. Construction of nonlinear filter banks has been discussed, but the question of how to utilize the nonlinearities remained. We answer this question by describing our transform via lifting. Lifting provides a spatial domain framework for the wavelet transform. In the lifting formalism, wavelet coefficients are seen as prediction residuals from a linear prediction operation. Wavelet coefficients are large near edges because the linear predictors are built to interpolate low order polynomials. Our goal is to avoid this problem by adapting the predictor based on local image properties. In smooth regions of the image, we use high order polynomial predictors. We adaptively reduce the prediction order to avoid attempting to predict values across discontinuities.
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    Nonlinear Wavelet Transforms for Image Coding
    (1997-11-01) Claypoole, Roger L.; Davis, Geoffrey; Sweldens, Wim; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We examine the central issues of invertibility, stability, artifacts, and frequency-domain characteristics in the construction of nonlinear analogs of the wavelet transform. The lifting framework for wavelet construction motivates our analysis and provides new insight into the problem. We describe a new type of nonlinearity for use in constructing nonlinear transforms: a set of linear predictors that are chosen adaptively using a nonlinear selection function. We also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood. We present preliminary results for a synthetic test image.
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    Nonlinear Wavelet Transforms for Image Coding via Lifting
    (2003-12-01) Claypoole, Roger L.; Davis, Geoffrey; Sweldens, Wim; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We investigate central issues such as invertibility, stability, synchronization, and frequency characteristics for nonlinear wavelet transforms built using the lifting framework. The nonlinearity comes from adaptively choosing between a class of linear predictors within the lifting framework. We also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood of pixels. Preliminary compression results for model and real-world images demonstrate the promise of our techniques.