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

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 straight forward to calculate. Our results show that the proposed distortion measure matches well with subjective evaluation found on human visual perception.

Publication
International Conference on Pattern Recognition (ICPR)
Haiping Lu
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead

I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.