  The optimization objective of regression-based blind image quality assessment
(IQA) models is to minimize the mean prediction error across the training
dataset, which can lead to biased parameter estimation due to potential
training data biases. To mitigate this issue, we propose a regression-free
framework for image quality evaluation, which is based upon retrieving locally
similar instances by incorporating semantic and distortion feature spaces. The
approach is motivated by the observation that the human visual system (HVS)
exhibits analogous perceptual responses to semantically similar image contents
impaired by identical distortions, which we term as content-distortion
consistency. The proposed method constructs a hierarchical k-nearest neighbor
(k-NN) algorithm for instance retrieval through two classification modules:
semantic classification (SC) module and distortion classification (DC) module.
Given a test image and an IQA database, the SC module retrieves multiple
pristine images semantically similar to the test image. The DC module then
retrieves instances based on distortion similarity from the distorted images
that correspond to each retrieved pristine image. Finally, quality prediction
is obtained by aggregating the subjective scores of the retrieved instances.
Without training on subjective quality scores, the proposed regression-free
method achieves competitive, even superior performance compared to
state-of-the-art regression-based methods on authentic and synthetic distortion
IQA benchmarks.
