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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 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 languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages1553-1556
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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