A no reference image quality assessment method based on RepVGG
Xiaosheng Huang, jun-an pan, Runtao Duan · Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021) · 2022
Most state-of-art deep learning based no reference image quality assessment (NR IQA) methods usually have a complex network structure which are memory-consuming, hard to train and so not applicable in practical scenarios. Aim at these problems, a RepVGG based NR-IQA algorithm with transfer learning is proposed. The method uses ImageNet dataset to pre-train RepVGG network to get network parameters, and then uses the trained network to extract image features of image quality assessment data set. Finally, a simple fully connected network is trained to get the quality score of the image based on these features. The experimental results show that on the KADID-10K, LIVE、TID2013 and CSID datasets, the overall objective assessment obtained by the method is better than the state-of-art deep learning-based methods with good consistency with the subjective assessment.