In image quality assessment, regarding the issue that the Transformer model is unable to capture features of images across varying scales and positions, a hybrid model based on Res2Net and ViT (Vision Transformer) for no-reference image quality assessment is proposed. By leveraging the multi-scale and cross-scale connections of Res2Net, the model expands the receptive field and enhances feature representation capabilities, thereby obtaining more helpful image details. The feature images generated by Res2Net replace the original image patches as Transformer inputs, and the Transformer are used to capture global feature, so that the hybrid model can balance the detail information and global information of the image. Experiments are conducted on both real-world distortion datasets and synthetic distortion datasets. The results show that the proposed model exhibits excellent assessment performance and possesses certain generalization ability, the overall performance is superior to other CNN-based models.