An objective distortion measure for binary document images based on human visual perception

Haiping Lu, Jian Wang, Alex C. Kot, Yun Q. Shi

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

As we are moving to a digital world, digital document image processing is receiving more and more attention. Digital document images are essentially binary images. In applications related to binary document images, such as data hiding and watermarking in binary images, distortion may be present and it is necessary to measure the distortion for performance comparison. However, traditional objective distortion measures cannot describe the distortion in binary images well to have a good match with human visual perception. In this paper, we present a novel objective distortion measure for binary document images that well correlates to the subjective distortion perception. This measure is based on the reciprocal of distance that is straightforward to calculate. Our results show that the proposed distortion measure matches well with subjective evaluation found on human visual perception.

Original languageEnglish (US)
Pages (from-to)239-242
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume16
Issue number4
StatePublished - Dec 1 2002

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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