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Image quality assessment (IQA) is an active research area in the field ofimage processing. Most prior works focus on visual quality of natural imagescaptured by cameras. In this paper, we explore visual quality of scanneddocuments, focusing on raster image areas. Different from many existing workswhich aim to estimate a visual quality score, we propose a machine learningbased classification method to determine whether the visual quality of ascanned raster image at a given resolution setting is acceptable. We conduct apsychophysical study to determine the acceptability at different imageresolutions based on human subject ratings and use them as the ground truth totrain our machine learning model. However, this dataset is unbalanced as mostimages were rated as visually acceptable. To address the data imbalanceproblem, we introduce several noise models to simulate the degradation of imagequality during the scanning process. Our results show that by includingaugmented data in training, we can significantly improve the performance of theclassifier to determine whether the visual quality of raster images in ascanned document is acceptable or not for a given resolution setting.

A Visual Quality Assessment Method for Raster Images in Scanned Document