Metric-based No-reference Quality Assessment of Heterogeneous Document Images
Résumé
No-reference image quality assessment (NR-IQA) aims at computing an image quality score that best correlates
with either human perceived image quality or an objective quality measure, without any prior knowledge of
reference images. Although learning-based NR-IQA methods have achieved the best state-of-the-art results so
far, those methods perform well only on the datasets on which they were trained. The datasets usually contain
homogeneous documents, whereas in reality, document images come from different sources. It is unrealistic to
collect training samples of images from every possible capturing device and every document type. Hence, we
argue that a metric-based IQA method is more suitable for heterogeneous documents. We propose a NR-IQA
method with the objective quality measure of OCR accuracy. The method combines distortion-specific quality
metrics. The final quality score is calculated taking into account the proportions of, and the dependency among
different distortions. Experimental results show that the method achieves competitive results with learning-based
NR-IQA methods on standard datasets, and performs better on heterogeneous documents.