A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling

Sheng Hua Zhong; Yan Liu; Yang Liu; Fu Lai Korris Chung

doi:10.1109/ICIP.2010.5653807

A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling

Sheng Hua Zhong, Yan Liu, Yang Liu, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

21 Citations (Scopus)

Abstract

This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eyetracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic meaning from tag information to learn a top-down & bottom-up saliency model based on the eye-tracking data. Empirical validations on standard dataset demonstrate the effectiveness of the proposed model and metric.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	1553-1556
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5653807
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

Image quality assessment
No-reference
Top-down & bottom-up saliency map

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5653807

Cite this

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title = "A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling",

abstract = "This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eyetracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic meaning from tag information to learn a top-down & bottom-up saliency model based on the eye-tracking data. Empirical validations on standard dataset demonstrate the effectiveness of the proposed model and metric.",

keywords = "Image quality assessment, No-reference, Top-down & bottom-up saliency map",

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doi = "10.1109/ICIP.2010.5653807",

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Zhong, SH, Liu, Y, Liu, Y & Chung, FLK 2010, A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5653807, pp. 1553-1556, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 26/09/10. https://doi.org/10.1109/ICIP.2010.5653807

A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling. / Zhong, Sheng Hua; Liu, Yan; Liu, Yang et al.
2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings. 2010. p. 1553-1556 5653807.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eyetracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic meaning from tag information to learn a top-down & bottom-up saliency model based on the eye-tracking data. Empirical validations on standard dataset demonstrate the effectiveness of the proposed model and metric.

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A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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