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Learning-based image quality assessment (IQA) has made remarkable progress inthe past decade, but nearly all consider the two key components -- model anddata -- 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 conductingpsychophysical experiments to construct ``better'' human-annotated datasets,which unfortunately ignores current IQA models during dataset creation. In thispaper, we first design a series of experiments to probe computationally thatsuch isolation of model and data impedes further progress of IQA. We thendescribe a computational framework that integrates model-centric anddata-centric IQA. As a specific example, we design computational modules toquantify the sampling-worthiness of candidate images. Experimental results showthat the proposed sampling-worthiness module successfully spots diversefailures of the examined blind IQA models, which are indeed worthy samples tobe included in next-generation datasets.

Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches