A lightweight approach for image quality assessment

Image quality assessment is a vital computer vision task for image validation and visual experience development. Lately, most research in this field has focused on enhancing model performance, resulting in a significant growth in model size. Those models may require considerable storage resources and computational costs. Additionally, they have yet to focus on the small-parameter models. Therefore, to contribute a lightweight model for this topic, this paper proposes a new module called GhostDPD and uses it to construct a MobileDPD model. The GhostDPD structure has lightweight attention layers, two depth-wise convolutional layers, and a module with fewer parameters to replace the point-wise layer. Experiments with the different datasets showed that the proposed model achieved similar results to state-of-the-art approaches despite being much smaller.