Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Foreground Subtraction

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Adaptive extreme video completion

Sabine Süsstrunk, Radhakrishna Achanta, Majed El Helou, Ruofan Zhou

A computer-implemented video completion method is proposed for reconstructing a sparsely sampled video comprising sparsely sampled video frames comprising sparse sets of picture element intensity values. The method comprises: distributing the sparse sets o ...

2021

Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based Image Coding

Touradj Ebrahimi, Michela Testolina, Evgeniy Upenik, Fernando Joaquim Leite Pereira

Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG~2000 and HEIC. In this paper, a crowdsourcing based subjective quality e ...

IEEE2021

On Block Prediction For Learning-Based Point Cloud Compression

Touradj Ebrahimi, Evangelos Alexiou, Davi Nachtigall Lazzarotto

Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardizati ...

IEEE2021

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Mathew Magimai Doss

Information processing by a population of neurons is studied using two-photon calcium imaging techniques. A neuronal spike results in an increased intracellular calcium concentration. Fluorescent calcium indicators change their brightness upon a change in ...

2021

Convex optimization in sums of Banach spaces

Michaël Unser, Shayan Aziznejad

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

2021

Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning

Arman Iranfar

In my thesis, I reveal several already-existing and emerging challenges in multi-objective management of multiprocessor systems, and address them through novel solutions, from heuristics to RL, depending on the complexity of the problem. Conventional multi ...

EPFL2020

Image Compression and Quality Assessment using Convolutional Neural Networks

Pinar Akyazi

The rapid development of digital imaging and video has placed visual contents in the heart of our lives. Digital multimedia span a vast number of areas from business to leisure, including but not limited to education, medicine, accessibility, training, adv ...

EPFL2020

Sparse and Parametric Modeling with Applications to Acoustics and Audio

Helena Peic Tukuljac

Recent advances in signal processing, machine learning and deep learning with sparse intrinsic structure of data have paved the path for solving inverse problems in acoustics and audio. The main task of this thesis was to bridge the gap between the powerfu ...

EPFL2020

Dynamic Model Pruning with Feedback

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Luis Felipe Barba Flores

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

2020

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

Georges Wagnières

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vis ...

Nature Research2020

On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication

Na Li

In distributed optimization and machine learning, multiple nodes coordinate to solve large problems. To do this, the nodes need to compress important algorithm information to bits so that it can be communicated over a digital channel. The communication tim ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020