Multiview videos plus depth (MVD) is a popular 3D video representation where pixel depth information is exploited to generate additional views to provide 3D experience. Quality assessment of MVD data is of paramount importance since the latest research results show that existing 2D quality metrics are not suitable for MVD. This paper focuses on depth quality assessment and presents a novel algorithm to estimate the distortion in depth videos induced by compression. The proposed algorithm is noreference and does not require any prior training or modeling. The proposed method is based solely on the statistical analysis of the compression sensitive pixels of depth images. The experimental results worked out on a standard MVD dataset show that the proposed algorithm exhibits a very high correlation with conventional full-reference metrics.

Blind depth quality assessment using histogram shape analysis

FARID, MUHAMMAD SHAHID;LUCENTEFORTE, Maurizio;GRANGETTO, Marco
2015

Abstract

Multiview videos plus depth (MVD) is a popular 3D video representation where pixel depth information is exploited to generate additional views to provide 3D experience. Quality assessment of MVD data is of paramount importance since the latest research results show that existing 2D quality metrics are not suitable for MVD. This paper focuses on depth quality assessment and presents a novel algorithm to estimate the distortion in depth videos induced by compression. The proposed algorithm is noreference and does not require any prior training or modeling. The proposed method is based solely on the statistical analysis of the compression sensitive pixels of depth images. The experimental results worked out on a standard MVD dataset show that the proposed algorithm exhibits a very high correlation with conventional full-reference metrics.
3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Lisbon
8-10 July 2015
3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2015
IEEE
1
1
5
978-1-4673-8090-4
978-1-4673-8090-4
Farid, Muhammad Shahid; Lucenteforte, Maurizio; Grangetto, Marco
File in questo prodotto:
File Dimensione Formato  
main.pdf

Accesso aperto

Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 1.12 MB
Formato Adobe PDF
1.12 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1531310
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 0
social impact