Math @ Duke
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Publications [#287160] of Ingrid Daubechies
Papers Published
- Cohen, A; Daubechies, I; Guleryuz, OG; Orchard, MT, On the importance of combining wavelet-based nonlinear approximation with coding strategies,
IEEE Transactions on Information Theory, vol. 48 no. 7
(July, 2002),
pp. 1895-1921, Institute of Electrical and Electronics Engineers (IEEE), ISSN 0018-9448 [doi]
(last updated on 2024/03/28)
Abstract: This paper provides a mathematical analysis of transform compression in its relationship to linear and nonlinear approximation theory. Contrasting linear and nonlinear approximation spaces, we show that there are interesting classes of functions/random processes which are much more compactly represented by wavelet-based nonlinear approximation. These classes include locally smooth signals that have singularities, and provide a model for many signals encountered in practice, in particular for images. However, we also show that nonlinear approximation results do not always translate to efficient compression strategies in a rate-distortion sense. Based on this observation, we construct compression techniques and formulate the family of functions/stochastic processes for which they provide efficient descriptions in a rate-distortion sense. We show that this family invariably leads to Besov spaces, yielding a natural relationship among Besov smoothness, linear/nonlinear approximation order, and compression performance in a rate-distortion sense. The designed compression techniques show similarities to modern high-performance transform codecs, allowing us to establish relevant rate-distortion estimates and identify performance limits.
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