A no reference image quality assessment method based on RepVGG

Xiaosheng Huang; Jun-an Pan; Runtao Duan

doi:10.1117/12.2623451

16 February 2022 A no reference image quality assessment method based on RepVGG

Xiaosheng Huang, Jun-an Pan, Runtao Duan

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208322 (2022) https://doi.org/10.1117/12.2623451
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

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.

Citation Download Citation

Xiaosheng Huang, Jun-an Pan, and Runtao Duan "A no reference image quality assessment method based on RepVGG", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208322 (16 February 2022); https://doi.org/10.1117/12.2623451

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