Abstract

In the paper, a novel hybrid method for an automatic quality assessment of binary images is proposed that may be useful, e.g., for computationally limited embedded systems or Optical Character Recognition applications. Since the quality of binary images used as the input for further image analysis strongly influences the obtained results, a reliable evaluation of their quality is a crucial element for the validation of such systems. Assuming the availability of several video frames, an objective quality assessment of individual video frames may also be helpful for the choice of a binarized image leading to the “best” final results. Nevertheless, most of the image quality assessment methods concern the analysis of grayscale or color images, utilizing the available image datasets containing numerous images subject to various distortions together with the corresponding subjective quality scores. Therefore, a reliable quality assessment of binary images is troublesome due to a small number of datasets and methods dedicated to binary images. The approach presented in the paper is based on the combination of one of such metrics, known as Border Distance, with some other methods, utilizing the nonlinear model based on the weighted sum and product of individual metrics. The experimental verification of the proposed hybrid metric conducted for the publicly available Bilevel Image Similarity Ground Truth Archive dataset leads to a significantly higher correlation with subjective quality scores in comparison to the other methods.

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