  Learning-based image quality assessment (IQA) has made remarkable progress in
the past decade, but nearly all consider the two key components -- model and
data -- in isolation. Specifically, model-centric IQA focuses on developing
``better'' objective quality methods on fixed and extensively reused datasets,
with a great danger of overfitting. Data-centric IQA involves conducting
psychophysical experiments to construct ``better'' human-annotated datasets,
which unfortunately ignores current IQA models during dataset creation. In this
paper, we first design a series of experiments to probe computationally that
such isolation of model and data impedes further progress of IQA. We then
describe a computational framework that integrates model-centric and
data-centric IQA. As a specific example, we design computational modules to
quantify the sampling-worthiness of candidate images. Experimental results show
that the proposed sampling-worthiness module successfully spots diverse
failures of the examined blind IQA models, which are indeed worthy samples to
be included in next-generation datasets.
