As a typical information medium, images are widely utilized across various scenarios. Measuring image quality accurately is meaningful for the subsequent usability of images. However, significant variations exist in image types and distortion types in different scenarios. And, acquiring labeled images for each specific scenario is time-consuming and labor-intensive. Consequently, designing cross-domain image quality assessment (IQA) that generalizes across different scenarios remains a substantial challenge. Existing cross-domain IQA methods primarily focus on content relevance while neglecting distortion differences, leading to limited applicability while distortion fluctuates. To address these limitations, a graph-driven domain co-adaptation framework for cross-domain IQA (GDCIQA) is proposed. Firstly, a graph knowledge sharing (GKS) module that constructs graphs via inter-domain distortion relevance has been proposed. GKS employs graph neural networks to update quality-aware features in the source domain by leveraging target-domain representations. Secondly, the proposed co-adaptation learning (CAL) mechanism can enable joint optimization of different modules, which ensures comprehensive sharing of quality-aware and distortion-related information. Finally, a domain adaptation framework has been designed to train models effectively on labeled source images, yielding target-domain-optimized IQA models. Experimental results demonstrate that GDCIQA achieves higher accuracy and stability in cross-domain scenarios. The proposed GKS and CAL can advance cross-domain IQA research.

Scoliosis significantly impacts quality of life, highlighting the need for effective early scoliosis screening (SS) and intervention. However, current SS methods often involve physical contact, undressing, or radiation exposure. This study introduces an innovative, non-invasive SS approach utilizing a monocular RGB camera that eliminates the need for undressing, sensor attachment, and radiation exposure. We introduce a novel approach that employs Parameterized Human 3D Reconstruction (PH3DR) to reconstruct 3D human models, thereby effectively eliminating clothing obstructions, seamlessly integrated with an ISANet segmentation network, which has been enhanced by Multi-Scale Fusion Attention (MSFA) module we proposed for facilitating the segmentation of distinct human trunk and limb features (HTLF), capturing body surface asymmetries related to scoliosis. Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. The experiments demonstrated that PH3DR and MSFA significantly improved the segmentation and extraction of HTLF, whereas ST-based CMU-Pose substantially enhanced the extraction of HSF. Our final model achieved a comparable F1 (0.895$\pm$0.014) to the best-performing baseline model, with only 0.79% of the parameters and 1.64% of the FLOPs, achieving 36 FPS-significantly higher than the best-performing baseline model (10 FPS). Moreover, our model outperformed two spine surgeons, one less experienced and the other moderately experienced. With its patient-friendly, privacy-preserving, and easily deployable solution, this approach is particularly well-suited for early SS and routine monitoring.

Most existing image quality assessment methods need to be retrained when dealing with a new type of task. This approach wastes computing resources and time. Therefore, these methods fail to suit the application scenarios that require processing of multi-type image quality assessment tasks. In the human visual system, the eyes of human tend to pay varying degrees of attention to different regions. Inspired by this system, this paper proposes a multi-type image quality assessment method based on multi-region deep feature fusion under meta-learning (MMQA). First, we utilize the differences in the structural information to screen out salient and non-salient regions. Second, a deep multi-stream network is designed to comprehensively consider and fuse different features related to the quality in salient regions, non-salient regions and the entire image. Third, meta-learning is applied to quickly learn and update the parameters of the model when facing new types of images. By summarizing the prior knowledge in the training of one type of task, the model can be quickly fine-tuned for other types of images. The experimental results demonstrate that the proposed method has advantages over the existing methods in generalization and robustness. Furthermore, the proposed method can adapt well to different distortion types and different image types quickly and accurately.

Image quality assessment (IQA) is a meaningful research topic to meet the increasing demand of high‐quality image. The degradation of image quality will cause changes in image structural information. Meanwhile, human visual system is sensitive to changes in structural information. This finding motivates us to utilise structural information for proposing IQA method which is consistent with human visual perception. Recently, IQA methods are mainly focused on individual image type, e.g. natural image or screen content image (SCI), thus, the authors proposed a novel no‐reference IQA method which can be suitable for both natural image and SCI. The proposed method is based on structural information analysis. For each image, they first obtain the grey‐scale fluctuation maps (GFMs) in four detection directions. After that, the grey‐scale fluctuation direction map (GFD) of certain image can be acquired via its GFMs. Based on the GFMs and GFD, the structural features of each image are extracted, and then collected and transformed to feature vectors. Subsequently, the IQA model is trained by support vector regression. The experimental results on the public databases demonstrate the proposed method can predict image quality accurately for both natural image and SCI, and the performance is competitive with prevalent methods.