A Visual Quality Assessment Method for Raster Images in Scanned Document

Image quality assessment (IQA) is an active research area in the field of image processing. Most prior works targeted the visual quality of natural images captured by cameras. In this paper, we shift the focus towards the visual quality of scanned documents, especially raster image areas. Different from many existing works that aim to estimate a visual quality score, we propose a machine learning based classification method to determine whether the visual quality of a scanned raster image at a given resolution setting is acceptable. We conduct a psychophysical study to determine the acceptability of different image resolutions based on human subject ratings and use them as the ground truth to train our machine learning model. However, this dataset is imbalanced as most images were rated as visually acceptable. To address the data imbalance problem, we introduce several noise models to simulate the degradation of image quality during the scanning process. Our results show that by including augmented data in training, we can significantly improve the performance of the classifier to determine whether the visual quality of raster images in a scanned document is acceptable or not for a given resolution setting.