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

• URL: http://arxiv.org/abs/2207.14769v2
• Date: Fri, 8 Dec 2023 10:36:21 GMT
• Title: Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches
• Authors: Peibei Cao, Dingquan Li, and Kede Ma
• Abstract summary: Learning-based image quality assessment (IQA) has made remarkable progress in the past decade. Nearly all consider the two key components -- model and data -- in isolation.
• Score: 20.931709027443706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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