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Signal-dependent wavelet transform and application to lossless image compression

ELECTRONICS LETTERS 2002年第4期38卷 170-172页

作者： Yoo, H Joeng, J Hanyang Univ Dept Elect Commun Engn Seoul 133791 South Korea

A signal-dependent wavelet transform based on the lifting scheme is proposed. The transform can be made reversible (i.e. an integer-to-integer transform). The reversible transform. followed by arithmetic coding, is applied to lossless image compression. Simulation results indicate that the proposed method is superior to the S + P method.

关键词： wavelet transforms arithmetic coding simulation lifting scheme data compression Image and video coding arithmetic codes reversible transform signal-dependent wavelet transform Integral transforms image coding S+P method integer-to-integer transform lossless image compression Computer vision and image processing techniques Image recognition

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A new lifting scheme for lossless image compression

A new lifting scheme for lossless image compression

引用

Conference on Applications of Digital Image Processing XXVII

作者： Hou, HS Aerosp Corp Los Angeles CA 90009 USA

ISBN: (纸本)0819454966

Since its first introduction, the lifting scheme(1,2) has become a powerful method to perform wavelet transforms and many other orthogonal transforms. Especially for integer-to-integer wavelet transforms, the lifting scheme is an indispensable tool for lossless image compression. Earlier work has shown that the number of lifting steps can have an impact on the transform performance.(3) The fidelity of integer-to-integer transforms depends entirely on how well they approximate their original wavelet transforms. The predominant source of errors is due to the rounding-off of the intermediate real result to an integer at each lifting step. Hence, a wavelet transform with a large number of lifting steps would automatically increase the approximation error. In the case of lossy compression, the approximation error is less important because it is usually masked out by the transform coefficient quantization error. However, in the case of lossless compression, the compression performance is certainly affected by the approximation error. Consequently, the number of lifting steps in a wavelet transform is a major concern. The new lifting method presented in this paper reduces the number of lifting steps substantially in lossless data compression. Thus, it also significantly improves the overall rounding errors incurred in the real-to-integer conversion process at each of the lifting steps. The improvement of the overall rounding errors is more pronounced in the integer-to-integer orthogonal wavelet transforms, but the improvement in the integer-to-integer biorthogonal wavelet transforms is also significant. In addition, as a dividend, the new lifting method further saves memory space and decreases signal delay. Many examples on popular wavelet transforms are included.

关键词： integer-to-integer transform image compression lifting method lossless image compression wavelet transform

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A fast adaptive lifting method for lossless hyperspectral data compression

A fast adaptive lifting method for lossless hyperspectral da...

引用

Conference on Applications of Digital Image Processing XXVII

作者： Hou, HS Aerosp Corp Los Angeles CA 90009 USA

ISBN: (纸本)0819454966

The real advantage of using a wavelet transform for image data compression is the power of adapting to local statistics of the pixels. In hyperspectral data, many but not all spectral planes are well correlated. In each spectral plane, the spatial data is composed of patches of relatively smooth areas segmented by edges. The smooth areas can be well compressed by a relatively long wavelet transform with a larger number of vanishing moments. However, for the regions around edges, shorter wavelet transforms are preferable. Despite the fact that the local statistics of both the spectral and spatial data change from pixel to pixel, almost all known image data compression algorithms use only one wavelet transform for the entire dataset. For example, the current international still image data compression standard, JPEG2000, has adopted the 5/3 wavelet transform as the default standard for lossless image data compression for all images. There is not a single wavelet filter that performs uniformly better than the others. Thus, it would be beneficial to use many types of wavelet filters based on local activities of the image. The selected wavelet transform can thus be best adapted to the content of the image locally. In this paper, we have derived a fast adaptive lifting scheme that can easily switch wavelet filters from one to the other. The adaptation is performed on a pixel-by-pixel basis, and it does not need any bookkeeping overhead. It is known that the lifting scheme is a fast and powerful tool to implement all wavelet transforms. Especially for integer-to-integer wavelet transforms, the lifting scheme is an indispensable tool for lossless image compression. Taking advantage of our newly developed lossless lifting scheme, the fast adaptive lifting algorithm presented in this paper not only saves two lifting steps but also improves accuracy compared to the conventional lifting scheme for lossless data compression. Moreover, our simulation results for ten two-dimensiona

关键词： adaptive lifting method integer-to-integer transform image compression lifting method lossless data compression wavelet transform

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