## 1 Introduction  
The growing availability and popularity of mobile devices and embedded systems equipped with visual sensors increase the interest in onboard computations, as well as the development of computation methods and models useful for fast processing using limited hardware resources. Such limitations, related to memory amount and computational power, often hinder the real-time image analysis that may be important in scenarios where another image may be captured after changes in some camera parameters or its location. Some examples of such systems may be related to video-based navigation in mobile robotics, video inspection and diagnostics, and optical text recognition. Images captured by cameras integrated with mobile devices are usually subject to various distortions, particularly considering their acquisition in unknown lighting conditions, including non-uniform illumination, presence of shadows, etc. Although in many applications related to object detection and classification, image segmentation, traffic monitoring, video surveillance, object recognition and tracking, etc., many such distortions may be efficiently eliminated or their influence is not critical, there are some areas where the analysis of natural images may be significantly influenced by them. An obvious example may be a non-uniformly illuminated document image containing many small details--alphanumerical characters in such a case. Since a typical image preprocessing operation in Optical Character Recognition (OCR) systems is the binarization of input images, its results strongly depend on the presence of shadows and some other illumination issues. Application of the simplest global thresholding methods, such as Otsu [1], does not lead to satisfactory binary images in such cases, affecting not only the readability of some characters but often converting some parts of the image, even containing some characters, into solid black areas or a white background. In some cases, even the application of more advanced adaptive binarization methods does not lead to satisfactory results ensuring correct text recognition during further processing stages. In some situations, users can capture another photo of a document image, but the problem lies in the relatively long time necessary for the OCR procedure, particularly if text recognition is applied as a cloud service with an additional transmission required. Therefore, a convenient solution would be the image quality assessment (IQA) conducted with a metric highly correlated with OCR accuracy, which might be calculated much faster than the execution time of the text recognition process. One of the possible solutions might be the application of no-reference (\"blind\") IQA methods for input images in a similar way to the approach proposed for the fault-tolerant video-based OCR in the paper [2]. Nevertheless, the computation of such metrics for color or greyscale input images, although less demanding than the OCR procedure, is still much slower than similar calculations for binary images. Furthermore, the text recognition results depend on the binarization method [3; 4; 5] that is applied between image quality assessment of such greyscale or color images and the OCR procedure. Therefore, a more appropriate solution is the application of the binary image quality assessment for the selected binarization method. Unfortunately, the development of IQA methods for binary images is not as advanced as for greyscale or color images where a variety of full-reference (FR), reduced-reference (RR), and no-reference (NR) metrics [6] have been proposed during recent years. Although some quality metrics have been proposed also for binary images [7; 8; 9; 10], they are based on the comparison of a binary image with the \"ground-truth\" (reference) image. Such a full-reference approach cannot be directly applied for unknown images captured by a camera due to unavailability of reference images.