IEICE TRANSACTIONS on Information
Hybrid Integration of Visual Attention Model into Image Quality Metric
Summary :
Integrating the visual attention (VA) model into an objective image quality metric is a rapidly evolving area in modern image quality assessment (IQA) research due to the significant opportunities the VA information presents. So far, in the literature, it has been suggested to use either a task-free saliency map or a quality-task one for the integration into quality metric. A hybrid integration approach which takes the advantages of both saliency maps is presented in this paper. We compare our hybrid integration scheme with existing integration schemes using simple quality metrics. Results show that the proposed method performs better than the previous techniques in terms of prediction accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.11 pp.2971-2973
- Publication Date
- 2014/11/01
- Publicized
- 2014/08/22
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8141
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Chanho JUNG
Electronics and Telecommunications Research Institute (ETRI)
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Chanho JUNG, "Hybrid Integration of Visual Attention Model into Image Quality Metric" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 11, pp. 2971-2973, November 2014, doi: 10.1587/transinf.2014EDL8141.
Abstract: Integrating the visual attention (VA) model into an objective image quality metric is a rapidly evolving area in modern image quality assessment (IQA) research due to the significant opportunities the VA information presents. So far, in the literature, it has been suggested to use either a task-free saliency map or a quality-task one for the integration into quality metric. A hybrid integration approach which takes the advantages of both saliency maps is presented in this paper. We compare our hybrid integration scheme with existing integration schemes using simple quality metrics. Results show that the proposed method performs better than the previous techniques in terms of prediction accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8141/_p
Copy
@ARTICLE{e97-d_11_2971,
author={Chanho JUNG, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Integration of Visual Attention Model into Image Quality Metric},
year={2014},
volume={E97-D},
number={11},
pages={2971-2973},
abstract={Integrating the visual attention (VA) model into an objective image quality metric is a rapidly evolving area in modern image quality assessment (IQA) research due to the significant opportunities the VA information presents. So far, in the literature, it has been suggested to use either a task-free saliency map or a quality-task one for the integration into quality metric. A hybrid integration approach which takes the advantages of both saliency maps is presented in this paper. We compare our hybrid integration scheme with existing integration schemes using simple quality metrics. Results show that the proposed method performs better than the previous techniques in terms of prediction accuracy.},
keywords={},
doi={10.1587/transinf.2014EDL8141},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Hybrid Integration of Visual Attention Model into Image Quality Metric
T2 - IEICE TRANSACTIONS on Information
SP - 2971
EP - 2973
AU - Chanho JUNG
PY - 2014
DO - 10.1587/transinf.2014EDL8141
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2014
AB - Integrating the visual attention (VA) model into an objective image quality metric is a rapidly evolving area in modern image quality assessment (IQA) research due to the significant opportunities the VA information presents. So far, in the literature, it has been suggested to use either a task-free saliency map or a quality-task one for the integration into quality metric. A hybrid integration approach which takes the advantages of both saliency maps is presented in this paper. We compare our hybrid integration scheme with existing integration schemes using simple quality metrics. Results show that the proposed method performs better than the previous techniques in terms of prediction accuracy.
ER -
English
