In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.

Ciocca, G., Corchs, S., Gasparini, F. (2017). A complexity-based image analysis to investigate interference between distortions and image contents in image quality assessment. Intervento presentato a: International Workshop on Computational Color Imaging, CCIW 2017 29-31 march, Milan, Italy [10.1007/978-3-319-56010-6_9].

A complexity-based image analysis to investigate interference between distortions and image contents in image quality assessment

CIOCCA, GIANLUIGI
Primo
;
CORCHS, SILVIA ELENA
Secondo
;
GASPARINI, FRANCESCA
Ultimo
2017

Abstract

In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.
slide + paper
Full Reference metrics; Image complexity; Image Quality Assessment; Theoretical Computer Science; Computer Science (all)
English
International Workshop on Computational Color Imaging, CCIW 2017 29-31 march
2017
9783319560090
2017
10213
105
121
http://springerlink.com/content/0302-9743/copyright/2005/
none
Ciocca, G., Corchs, S., Gasparini, F. (2017). A complexity-based image analysis to investigate interference between distortions and image contents in image quality assessment. Intervento presentato a: International Workshop on Computational Color Imaging, CCIW 2017 29-31 march, Milan, Italy [10.1007/978-3-319-56010-6_9].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/10281/151351
Citazioni
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
Social impact