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文件名称：Learning to rank for blind image quality
文件大小：1.54MB
文件格式：PDF
更新时间：2021-07-14 10:32:00
图像质量评价
Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without
access to reference images. State-of-the-art BIQA methods typically require subjects to score a large
number of images to train a robust model. However, the acquisition of image quality scores has several
limitations: 1) scores are not precise, because subjects are usually uncertain about which score most
precisely represents the perceptual quality of a given image; 2) subjective judgments of quality may be
biased by image content; 3) the quality scales between different distortion categories are inconsistent,
because images corrupted by different types of distortion are evaluated independently in subjective
experiments; and 4) it is challenging to obtain a large scale database, or to extend existing databases,
because of the inconvenience of collecting sufficient images associated with different kinds of distortion
that have diverse levels of degradation, training the subjects, conducting subjective experiments, and
realigning human quality evaluations.
To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such
as “the quality of image Ia is better than that of image Ib” for training a robust BIQA model. The
preference label, representing the relative quality of two images, is generally precise and consistent,
and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at
very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of
learning the mapping from the image features to the preference label as one of classification. In particular,
we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to
provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then
presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable
performance to state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended
to new distortion categories.