SHIVPRO

Saliency-aware High-resolution Video Processing

 Coordinatore UNIVERSITE DE RENNES I 

 Organization address address: RUE DU THABOR 2
city: RENNES CEDEX
postcode: 35065

contact info
Titolo: Ms.
Nome: Yolaine
Cognome: Bompays
Email: send email
Telefono: 33223233723
Fax: 33223235876

 Nazionalità Coordinatore France [FR]
 Totale costo 269˙096 €
 EC contributo 269˙096 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2011-IIF
 Funding Scheme MC-IIF
 Anno di inizio 2012
 Periodo (anno-mese-giorno) 2012-08-20   -   2014-08-19

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITE DE RENNES I

 Organization address address: RUE DU THABOR 2
city: RENNES CEDEX
postcode: 35065

contact info
Titolo: Ms.
Nome: Yolaine
Cognome: Bompays
Email: send email
Telefono: 33223233723
Fax: 33223235876

FR (RENNES CEDEX) coordinator 269˙096.40

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

objects    efficient    temporal    hr    model    regions    video    compression    representation    uniform    efficiently    resolution    multiscale    models    tree    saliency    retargeting    salient    videos   

 Obiettivo del progetto (Objective)

'The ever-increasing spatial/temporal resolution of video such as ultra high definition has raised new challenges on storage, transmission and display for business, home, and mobile video applications, and thus efficient representation, compression, and retargeting of high-resolution (HR) videos become the key issues for effective deployment of these applications. Saliency models can facilitate to address these issues, but in practice, the current state-of-the-art saliency models are insufficient for efficiently handling complicated scenes containing multi-scale non-homogenous objects, highly textured regions and cluttered background. The objective of this project is to propose an efficient spatiotemporal saliency model to predict salient regions in HR videos, and fully exploit it to ease the design and improve the performance of HR video compression and retargeting applications. With the aim to overcome the drawbacks of existing saliency models, based on a multiscale region representation, the proposed model systematically realizes statistical model saliency measuring, intra-scale saliency modification, inter-scale saliency propagation and flexible incorporation of top-down information, to generate a novel saliency representation form with scalability, saliency tree, from which a multiscale saliency fusion scheme is used to derive high-quality saliency maps at various scales. Saliency tree enables an efficient search of multiple salient objects, guides the temporal non-uniform downsampling, and directs the enhanced mode decision in HR video compression. Saliency diffusion, shrinkability/stretchability estimation, and an integration of cropping/warping and uniform scaling are exploited for HR video retargeting. The research results will consolidate Europe as a leader in the research domain of saliency modeling and saliency based applications, facilitate to promote the developments of HR video services in Europe, and thus boost the European competitiveness.'

Introduzione (Teaser)

Visual saliency refers to the subjective importance given to certain parts of an image that draws one's attention there. Improved saliency models will help to efficiently represent high-resolution video content by focusing processing 'attention'.

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