  Existing blind image quality assessment (BIQA) methods focus on designing
complicated networks based on convolutional neural networks (CNNs) or
transformer. In addition, some BIQA methods enhance the performance of the
model in a two-stage training manner. Despite the significant advancements,
these methods remarkably raise the parameter count of the model, thus requiring
more training time and computational resources. To tackle the above issues, we
propose a lightweight parallel framework (LPF) for BIQA. First, we extract the
visual features using a pre-trained feature extraction network. Furthermore, we
construct a simple yet effective feature embedding network (FEN) to transform
the visual features, aiming to generate the latent representations that contain
salient distortion information. To improve the robustness of the latent
representations, we present two novel self-supervised subtasks, including a
sample-level category prediction task and a batch-level quality comparison
task. The sample-level category prediction task is presented to help the model
with coarse-grained distortion perception. The batch-level quality comparison
task is formulated to enhance the training data and thus improve the robustness
of the latent representations. Finally, the latent representations are fed into
a distortion-aware quality regression network (DaQRN), which simulates the
human vision system (HVS) and thus generates accurate quality scores.
Experimental results on multiple benchmark datasets demonstrate that the
proposed method achieves superior performance over state-of-the-art approaches.
Moreover, extensive analyses prove that the proposed method has lower
computational complexity and faster convergence speed.
