Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard deviation pooling, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six datasets of natural images, a synthetic dataset, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at https://www.mathworks.com/matlabcentral/fileexchange/59809.
Self-Supervised Topologically Invariant Manifold Learning for Railway Image Quality Assessment
CRRC Chongqing Co., Ltd.（中车重庆有限公司）；College of Computer Science, Sichuan University（四川大学计算机学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University（重庆大学先进装备机械传动国家重点实验室）；College of Mathematics and Statistics, Chongqing University（重庆大学数学与统计学院）
Towards Trustworthy Dermatology MLLMs: A Benchmark and Multimodal Evaluator for Diagnostic Narratives
School of Data Science, The Chinese University of Hong Kong, Shenzhen（数据科学学院，香港中文大学（深圳））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Dermatology, The First Affiliated Hospital, Shantou University Medical College（皮肤科，汕头大学医学院第一附属医院）
MSD-Score: Multi-Scale Distributional Scoring for Reference-Free Image Caption Evaluation
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Computer Science and Technology and the State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University（北京交通大学计算机科学与技术学院和先进轨道交通自主运行国家重点实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Mechanical Engineering, Guizhou University（贵州大学机械工程学院）
A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline
Department of Nanobiophysics, Faculty of Science and Technology, University of Twente（特文特大学科学与技术学院生物医学系）；Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute（荷兰癌症研究所手术引导外科部）；Netherlands Cancer Institute（荷兰癌症研究所）；Center for Early Cancer Detection, Netherlands Cancer Institute（荷兰癌症研究所早期癌症检测中心）
BlackMirror: Black-Box Backdoor Detection for Text-to-Image Models via Instruction-Response Deviation
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；BDKM, University of Chinese Academy of Sciences（中国科学院大学BDKM）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Cyber Science and Tech., Shenzhen Campus, Sun Yat-sen University（中山大学深圳校区网络安全科学与技术学院）
