  Nowadays, most existing blind image quality assessment (BIQA) models 1) are
developed for synthetically-distorted images and often generalize poorly to
authentic ones; 2) heavily rely on human ratings, which are prohibitively
labor-expensive to collect. Here, we propose an $opinion$-$free$ BIQA method
that learns from synthetically-distorted images and multiple agents to assess
the perceptual quality of authentically-distorted ones captured in the wild
without relying on human labels. Specifically, we first assemble a large number
of image pairs from synthetically-distorted images and use a set of
full-reference image quality assessment (FR-IQA) models to assign pseudo-binary
labels of each pair indicating which image has higher quality as the
supervisory signal. We then train a convolutional neural network (CNN)-based
BIQA model to rank the perceptual quality, optimized for consistency with the
binary labels. Since there exists domain shift between the synthetically- and
authentically-distorted images, an unsupervised domain adaptation (UDA) module
is introduced to alleviate this issue. Extensive experiments demonstrate the
effectiveness of our proposed $opinion$-$free$ BIQA model, yielding
state-of-the-art performance in terms of correlation with human opinion scores,
as well as gMAD competition. Codes will be made publicly available upon
acceptance.
