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1998
Journal Article
Titel
Complexity-constrained best-basis wavelet packet algorithm for image compression
Abstract
The concept of adapted waveform analysis using a best-basis selection out of a predefined library of wavelet packet (WP) bases allows an efficient image representation for the purpose of compression. Image coding methods based on the best-basis WP representation have shown significant coding gains for some image classes compared with methods using a fixed dyadic structured wavelet basis, at the expense however, of considerably higher computational complexity. A modification of the best-basis method, the so-called complexity constrained best-basis algorithm (CCBB), is proposed which parameterises the complexity gap between the fast (standard) wavelet transform and the best wavelet packet basis of a maximal WP library. This new approach allows a 'suboptimal' best basis to be found with respect to a given budget of computational complexity or, in other words, it offers an instrument to control the trade-off between compression speed and, coding efficiency. Experimental results are presented for image coding applications showing a highly nonlinear relationship between the rate-distortion performance and the computational complexity in such a way that a relatively small increase in complexity with respect to the standard wavelet basis results in a relatively high rate distortion gain.
Tags
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computational complexity
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data compression
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discrete wavelet transforms
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image coding
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image representation
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rate distortion theory
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transform coding
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best-basis wavelet packet algorithm
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image compression
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adapted waveform analysis
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complexity constrained best-basis algorithm
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wavelet transformation
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wavelet packet basis
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suboptimal best basis
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coding efficiency
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compression speed
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rate-distortion performance