C02
Digital Representations of Manifold Data


Whenever data is processed using computers, analog-to-digital (A/D) conversion, that is, representing the data using just 0s and 1s, is a crucial ingredient of the procedure. This project will mathematically study approaches to this problem for data with geometric structural constraints.

Mission-

Investigating how well geometric objects can be represented using bit streams

Scientific Details+

Many results in mathematical data analysis follow the paradigm of increasing efficiency by incorporating structural assumptions on the underlying data. The most prominent models today are sparsity models and manifold models in high dimensional data analysis. One main goal in these scenarios is a faithful analog to digital conversion, which not only requires a discretization, but also a digitalization step, often termed quantization, as only a finite number of bits can be processed.

A key aspect in finding suitable methods for digitally representing data is a careful balance between the resolution of this quantization step and the redundancy of the underlying discretization. Arguably the most popular class of suitable quantization schemes for highly redundant settings are so-called Sigma-Delta modulators. The underlying idea is to quantize recursively and then deduce recovery guarantees from the stability of the resulting discrete dynamical system. Variants are available for bandlimited signals, for frame representations, and for compressed sensing. For manifold models, however, there is very little quantization literature available. Project C02 aims to fill this gap, attempting a systematic study of quantization under manifold constraints.

The following three viewpoints shall be investigated:

  • Functions on manifolds: This work package is devoted to quantized representations of functions whose domain is a manifold. We will study bandlimited functions and also discuss applications to digital halftoning on manifolds.
  • Data lying on a manifold: We aim to study redundancies resulting from the fact that high dimensional data lies on a or close to a known manifold. We aim to incorporate quantization into the compressed sensing methodology for manifold models, as they have recently received attention in the literature.

Geometric and topological properties: Our question here is to what extent the manifold as a whole or its important geometric and topological properties can be recovered despite quantization. This project part will closely interact with project C04, which studies a related problem without quantization.

Publications+

Papers
Predicting sparse circle maps from their dynamics

Authors: Krahmer, F. and Kuehn, C. and Sissouno, N.
Note: preprint
Date: Jan 2020
Download: arXiv

One-Bit Sigma-Delta modulation on the circle

Authors: Graf, Olga and Krahmer, Felix and Krause-Solberg, Sara
Note: preprint
Date: Nov 2019
Download: internal

One-Bit Unlimited Sampling

Authors: Graf, Olga and Bhandari, Ayush and Krahmer, Felix
In Proceedings: Proc. IEEE Intl. Conf. Acoustics Speach Signal Proc. (ICASSP 2019)
Date: Sep 2019
DOI: 10.1109/ICASSP.2019.8683266
Download: internal external

Robust One-bit Compressed Sensing With Manifold Data

Authors: Iwen, M. and Dirksen, S. and Maly, J. and Krause-Solberg, S.
In Proceedings: Proc. Intl. Conf. on Sampling Theory and Applications (SampTA '19)
Date: Sep 2019
Download: external

Approximation of generalized ridge functions in high dimensions

Author: Keiper, Sandra
Journal: Journal of Approximation Theory, 245:101--129
Date: 2019
DOI: 10.1016/j.jat.2019.04.006
Download: external

Higher order 1-bit Sigma-Delta modulation on a circle

Authors: Graf, O. and Krahmer, F. and Krause-Solberg, S.
In Proceedings: Proc. Intl. Conf. on Sampling Theory and Applications (SampTA '19)
Date: 2019
Download: external

On Unlimited Sampling and Reconstruction

Authors: Bhandari, Ayush and Krahmer, Felix and Raskar, Ramesh
Note: Preprint
Date: 2019
Download: arXiv

Predicting sparse circle maps from their dynamics

Authors: Krahmer, F. and Kühn, C. and Sissouno, N.
Note: preprint
Date: 2019
Download: arXiv

Recovery of binary sparse signals with biased measurement matrices

Authors: Flinth, Axel and Keiper, Sandra
Journal: IEEE Transactions on Information Theory
Date: 2019
DOI: 10.1109/TIT.2019.2929192
Download: external

One-Bit Sigma-Delta Modulation on a Closed Loop

Authors: Krause-Solberg, Sara and Graf, Olga and Krahmer, Felix
Journal: 2018 IEEE Statistical Signal Processing Workshop (SSP), pages 208--212
Date: Aug 2018
DOI: 10.1109/SSP.2018.8450721
Download: external

Level-Dependent Interpolatory Hermite Subdivision Schemes and Wavelets

Authors: Mariantonia Cotronei, Caroline Moosmüller, Tomas Sauer and Sissouno, Nada
Journal: Constructive Approximation
Date: Jul 2018
DOI: 10.1007/s00365-018-9444-4
Download: external

On Recovery Guarantees for One-Bit Compressed Sensing on Manifolds

Authors: Iwen, Mark A. and Krahmer, Felix and Krause-Solberg, Sara and Maly, Johannes
Journal: preprint
Date: Jul 2018
Download: arXiv

Compressed Sensing for Analog Signals

Authors: Bodmann, B. and Flinth, A. and Kutyniok, G.
Note: preprint
Date: Mar 2018
Download: arXiv

A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment

Authors: Reisenhofer, R. and Bosse, S. and Kutyniok, G. and Wiegand, T.
Journal: Signal Proc. Image Comm., 61:33--43
Date: Feb 2018
DOI: 10.1016/j.image.2017.11.001
Download: external arXiv

Compressed sensing for finite-valued signals

Authors: Keiper, S. and Kutyniok, G. and Lee, D. G. and Pfander, G. E.
Journal: Linear Algebra and its Applications, 532:570--613
Date: Nov 2017
DOI: 10.1016/j.laa.2017.07.006
Download: external arXiv

$\ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed?

Authors: Genzel, M. and Kutyniok, G. and März, M.
Note: preprint
Date: Oct 2017


Posters
One-bit sigma-delta modulation on a closed loop

Author: Sara Krause-Solberg, Olga Graf, Felix Krahmer
Note: poster
Date: Nov 2018
Download: internal


Team+

Prof. Dr. Felix Krahmer    +

Projects: C02
University: TU München
E-Mail: Felix.Krahmer[at]ma.tum.de


Prof. Dr. Gitta Kutyniok   +

Projects: C03, C02
University: TU Berlin
E-Mail: kutyniok[at]math.tu-berlin.de
Website: http://www.math.tu-berlin.de/?108957


Olga Graf   +

Projects: C02
University: TU München
E-Mail: graf[at]ma.tum.de