SHIVPRO

Saliency-aware High-resolution Video Processing

 Coordinatore Shanghai University 

 Organization address address: Shangda Road 99
city: Shanghai
postcode: 200444

contact info
Titolo: Ms.
Nome: Jun
Cognome: Ge
Email: send email
Telefono: +86 21 56331809
Fax: +86 21 56333049

 Nazionalità Coordinatore China [CN]
 Totale costo 15˙000 €
 EC contributo 15˙000 €
 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-IIFR
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-09-13   -   2015-09-12

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    Shanghai University

 Organization address address: Shangda Road 99
city: Shanghai
postcode: 200444

contact info
Titolo: Ms.
Nome: Jun
Cognome: Ge
Email: send email
Telefono: +86 21 56331809
Fax: +86 21 56333049

CN (Shanghai) coordinator 15˙000.00

Mappa


 Word cloud

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

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

 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.'

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