The automatic assessment of perceived image quality is crucial in the field of image processing. To achieve this idea, we propose an image quality assessment (IQA) method for blurriness. The features of gradient and singular value were extracted in this method instead of the single feature in the traditional IQA algorithms. According to the insufficient size of existing public image quality assessment datasets to support deep learning, machine learning was introduced to fuse the features of multiple domains, and a new no-reference (NR) IQA method for blurriness denoted Feature fusion IQA(Ffu-IQA) was proposed. The Ffu-IQA uses a probabilistic model to estimate the probability of each edge detection blur in the image, and then uses machine learning to aggregate the probability information to obtain the edge quality score. After that uses the singular value obtained by singular value decomposition of the image matrix to calculate the singular value score. Finally, machine learning pooling is used to obtain the true quality score. Ffu-IQA achieves PLCC scores of 0.9570 and 0.9616 on CSIQ and TID2013, respectively, and SROCC scores of 0.9380 and 0.9531, which are better than most traditional image quality assessment methods for blurriness.

Convolutional- and Transformer-based backbone architecture are two dominant, widely accepted, models in computer vision. Nevertheless, it is still a challenge, thus a focus of research, to decide which backbone architecture performs better, and under which circumstances. In this paper, we conduct an in-depth investigation into the differences of the macroscopic backbone design of the CNN and Transformer models with the ultimate purpose of developing new models to combine the strengths of both types of architectures for effective image classification. Specifically, we first analyze the model structures of both models and identified four main differences, then we design four sets of ablation experiments using the ImageNet-1K dataset with an image classification problem as an example to study the impacts of these four differences on model performance. Based on the experimental results, we derive four observations as rules of thumb for designing a vision model backbone architecture. Informed by the experiment findings, we then conceive a novel model called CMNet which marries the experiment-proved best design practices of CNN and Transformer architectures. Finally, we carry out extensive experiments on CMNet using the same dataset against baseline classifiers. Initial results prove CMNet achieves the highest top-1 accuracy of 80.08% on the ImageNet-1K validation set, this is a very competitive value compared to previous classical models with similar computational complexity. Details of the implementation, algorithms and codes, are publicly available on Github: https://github.com/Arwin-Yu/CMNet.