Blind Synthetic Image Quality Assessment Using EfficientNet-V2 - Gabhane, Yogita; Jain, Tapan Kumar; Kamble, Vipin (International Journal of Intelligent Systems and Applications in Engineering, 2023)
We propose a simple and efficient deep convolutional neural network (CNN) model using EfficientNet-V2 S for Blind Image Quality Assessment (BIQA) that works for the distorted images from the TID2013 dataset consisting of images subjected to different varieties and levels of distortion. The ability of the EfficientNet-V2 S over its base model is effectively used for faster training and lower computational and time complexity. For quality assessment, the image is resized and normalized in such a manner as to retain the information content. The pre-trained model is used to evaluate weights using transfer learning over 80% of the TID2013 datasets while the model is tested on the remaining 20% of the distorted images. The actual scores and the predicted scores are compared based on two correlation coefficients namely Spearman’s Rank Order Correlation Coefficient (SROCC) and Pearson Linear Correlation Coefficient (PLCC) with state-of-the-art techniques used for BIQA. We fine-tuned the model by modifying the structure of the network especially the last layers for the target dataset and achieved remarkable performance for sixteen cases from 24 varied distortion cases.