The challenge of compressing and processing big data

Title of the research project

CRISP - Towards Compressive Information Processing Systems

 

Scientific area 

Signal processing, Computer Science, Electronics

Project coordinator

Enrico Magli

Abstract  

CRISP project targets the emerging frontier research field of compressive sensing (CS), and particularly its application in the framework of complex information processing systems, including vision systems and big data. CS is a breakthrough technology that will have a profound impact on how these systems are conceived.

 

Description of the research project 

The concept of information permeates our lives, as we continuously create, exchange and consume information in our relation with other people or for entertainment. It becomes more and more important to establish efficient ways to acquire, process, communicate and store large amounts of information.

Compressed Sensing (CS) is a new signal sensing/processing paradigm that goes beyond the traditional Shannon sampling theorem, which stated that signals can be reconstructed exactly if they are sampled with an adequate density. CS exploits the intuitive notion that most signals (such as speech, images, video sequences) are highly correlated, and offers a viable and elegant solution to acquisition, handling and processing of big data. This allows to dramatically reduce communication, storage and processing requirements, which makes CS one of the topics that will dominate signal processing research in the next years.

At the core of the CRISP research project is the concept of employing CS inside a complex information processing system.

The project will develop the technologies needed to move from sample-based to measurement-based information processing systems. The main challenge is to develop theory and algorithms that will allow to perform all signal manipulations typical of conventional systems (compression, encryption, communication, reconstruction, signal analysis, information extraction and decision) directly on the linear measurements, as reconstructing the signal would be unfeasible due to excessive complexity.

This leads to a very multidisciplinary and technically challenging research agenda.

 

Impact on society 

Future systems will have to handle unprecedented amounts of information such as those generated in multiview video or medical imaging applications. CRISP research aims at developing and demonstrating the fundamental tools that will fuel next-generation information processing systems with significantly better performance at a lower cost than today. 

 

Short CV of project coordinator 

Enrico Magli received the degree in Electronics Engineering and the Ph.D. degree in Electronics and Communications Engineering, both at Politecnico di Torino. He holds an Associate Professor position in the Dept. of Electronics of the same University.

His research activities are in the field of compressed sensing, error resilient image and video coding, compression of remote sensing images, distributed source coding, and image/video security. He has coauthored over 130 scientific papers in international journals and conferences, including more than 50 journal papers, and has organized several journal special issues and conference special sessions.

Working group 

At Politecnico di Torino: Tiziano Bianchi, Giulio Coluccia, Sophie Fosson, Chiara Ravazzi, Matteo Testa, Diego Valsesia, Tomas Björklund

Former members: Valerio Bioglio, Attilio Fiandrotti

Ongoing collaborations with Telecom Italia, European Space Agency, Politecnico di Milano, Università di Siena, INRIA, University of Illinois, Chicago, Centre Tecnològic Telecomunicacions Catalunya, Newcom (EU Network of Excellence in Wireless Communications)

    CRISP project has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework  Programme FP7 2007-2013,  grant agreement No 279848

  • Budget: 1.390.000
  • Start date: 1/07/2011
  • End date: 30/07/2017