71 research outputs found
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 focus on visual quality of natural images
captured by cameras. In this paper, we explore visual quality of scanned
documents, focusing on raster image areas. Different from many existing works
which 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 at 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 unbalanced 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
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