Image quality assessment (IQA) aims to measure the consistency between an objective algorithm output and a subjective perception measurement. This article focuses on this complex relationship in the context of color image scenarios—color image quality assessment (CIQA). This review systematically investigates CIQA applications in image compression, processing optimization, and domain-specific scenarios, analyzes benchmark datasets and assessment metrics, and categorizes CIQA algorithms into full-reference (FR), reduced-reference (RR) and no-reference (NR) methods. In this study, color images are evaluated using a newly developed CIQA framework. Focusing on FR and NR methods, FR methods leverage reference images with machine learning, visual perception models, and mathematical frameworks, while NR methods utilize distortion-only features through feature fusion and extraction techniques. Specialized CIQA algorithms are developed for robotics, low-light, and underwater imaging. Despite progress, challenges remain in cross-domain adaptability, generalization, and contextualized assessment. Future directions may include prototype-based cross-domain adaptation, fidelity–structure balancing, spatiotemporal consistency integration, and CIQA–restoration synergy to meet emerging demands.

Visual object tracking has been a major research topic in the field of computer vision for many years. Object tracking aims to identify and localize objects of interest in subsequent frames, given the bounding box of the first frame. In addition, the object-tracking algorithms are also required to have robustness and real-time performance. These requirements create some unique challenges, which can easily become overfitting if given a very small training dataset of objects during offline training. On the other hand, if there are too many iterations in the model-optimization process during offline training or in the model-update process during online tracking, it will cause the problem of poor real-time performance. We address these problems by introducing a meta-learning method based on fast optimization. Our proposed tracking architecture mainly contains two parts, one is the base learner and the other is the meta learner. The base learner is primarily a target and background classifier, in addition, there is an object bounding box prediction regression network. The primary goal of a meta learner based on the transformer is to learn the representations used by the classifier. The accuracy of our proposed algorithm on OTB2015 and LaSOT is 0.930 and 0.688, respectively. Moreover, it performs well on VOT2018 and GOT-10k datasets. Combined with the comparative experiments on real-time performance, our algorithm is fast and robust.

Visual object tracking has been a fundamental topic in computer vision and many convolutional neural network (CNN) based trackers proposed in recent years have achieved state‐of‐the‐art performance, multi‐domain convolutional neural network (MDNet) is one of the most representative CNN‐based algorithms with high performance. In order to significantly improve the robustness and accuracy of the MDNet algorithm, a multi‐domain convolutional neural network based on spatial self‐attention information learning (SAMDNet) is proposed in this study. The authors use the spatial self‐attention module for the spatial information learning of the model. The spatial self‐attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions. Under the control of self‐learning parameters in this module, spatial attention information can be flexibly acquired. The authors also propose a novel interval loss term to solve the problem of different classes with the same semantics in the training data. Finally, an anomaly detection module is carefully designed for relocation after the algorithm completely lost the target. Extensive experiments on the object tracking benchmark (OTB) and the visual object tracking challenge (VOT) benchmarks show that the proposed tracker outperforming most of the state‐of‐art trackers.

Assessing the quality of Artificial Intelligence-Generated Content (AIGC) images remains a critical challenge, as conventional Image Quality Assessment (IQA) methods often fail to capture the semantic consistency between generated images and their textual prompts. This study aims to establish an interpretable and efficient multimodal framework for evaluating AIGC image quality. The research addresses three key scientific questions: how to leverage structured prompt semantics for more interpretable assessments, how to enable parameter-efficient yet accurate adaptation, and how to achieve unified handling of perceptual and semantic subtasks. To this end, we propose the Prompt-Enhanced Low-Rank Adaptation (PELA) framework, which integrates Hierarchical Prompt Engineering and Low-Rank Adaptation within a CLIP-based backbone. Hierarchical prompts encode multi-level semantics for fine-grained evaluation, while low-rank adaptation enables lightweight, task-specific optimization. Experiments conducted on AGIQA-1K, AGIQA-3K, and AIGCIQA-2023 datasets demonstrate that PELA achieves superior correlation with human perceptual judgments and sets new state-of-the-art results across multiple metrics. The findings confirm that combining structured prompt semantics with efficient adaptation offers a compact, interpretable, and scalable paradigm for multimodal image quality assessment.

Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCD dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention’s feature extraction and attention guidance are verified by ablation experiments.

This paper considers the following parabolic-elliptic chemotaxis-growth system with nonlinear diffusion $\left\{ \begin{array}{l}{u_t} = \nabla (D(u)\nabla u) - \nabla (\chi {u^q}\nabla v) + \mu u(1 - {u^\alpha }),\;\;\;\;\;\;\;\;& x \in \Omega ,{\mkern 1mu} {\mkern 1mu} t > 0,\\0 = \Delta v - v + {u^\gamma },\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;&x \in \Omega ,{\mkern 1mu} {\mkern 1mu} t > 0\end{array} \right.$ under homogeneous Neumann boundary conditions for some constants $q≥ 1$, $α>0$ and $γ≥ 1$, where $D(u)≥ c_D u^{m-1}$$(m≥ 1)$ for all $u>0$ and $D(u)>0$ for all $u≥ 0$, and $Ω\subset\mathbb{R}^N$ $(N≥ 1)$ is a bounded domain with smooth boundary. It is shown that when $ m>q+γ-\frac{2}{N}, \, \, \mathbf{or}$$ α>q+γ-1, \, \, \mathbf{or}$$α = q+γ-1\, \, {\rm{and}}\, \, μ>μ^*$, where $ {\mu ^*} = \left\{ \begin{array}{l}\begin{array}{*{20}{l}}{\frac{{(\alpha + 1 - m)N - 2}}{{(\alpha + 1 - m)N + 2(\alpha - \gamma )}}\chi ,\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{if}}~~{\mkern 1mu} {\mkern 1mu} m \le q + \gamma - \frac{2}{N},}\end{array}\\0,\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{if}}~~{\mkern 1mu} {\mkern 1mu} m > q + \gamma - \frac{2}{N},\end{array} \right.$ then the above system possesses a global bounded classical solution for any sufficiently smooth initial data. The results improve the results by Wang et al. (J. Differential Equations 256 (2014)) and generalize the results of Zheng (J. Differential Equations 259 (2015)) and Galakhov et al. (J. Differential Equations 261 (2016)).