Advances in image compression, storage, and display technologies have made high-quality images and videos widely accessible. At this level of quality, distinguishing between compressed and original content becomes difficult, highlighting the need for assessment methodologies that are sensitive to even the smallest visual quality differences. Conventional subjective visual quality assessments often use absolute category rating scales, ranging from ``excellent'' to ``bad''. While suitable for evaluating more pronounced distortions, these scales are inadequate for detecting subtle visual differences. The JPEG standardization project AIC is currently developing a subjective image quality assessment methodology for high-fidelity images. This paper presents the proposed assessment methods, a dataset of high-quality compressed images, and their corresponding crowdsourced visual quality ratings. It also outlines a data analysis approach that reconstructs quality scale values in just noticeable difference (JND) units. The assessment method uses boosting techniques on visual stimuli to help observers detect compression artifacts more clearly. This is followed by a rescaling process that adjusts the boosted quality values back to the original perceptual scale. This reconstruction yields a fine-grained, high-precision quality scale in JND units, providing more informative results for practical applications. The dataset and code to reproduce the results will be available at https://github.com/jpeg-aic/dataset-BTC-PTC-24.
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
University of Science and Technology Beijing（北京科技大学）；Microsoft Research（微软研究院）；Shunde Graduate School of University of Science and Technology Beijing（北京科技大学顺德研究生院）
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
University of Konstanz（康斯坦茨大学）；Cloudinary（Cloudinary公司）；IST-IT（IST-IT机构）；Fraunhofer IIS（弗劳恩霍夫应用固体物理研究所）；Huawei（华为公司）；Takushoku University（拓殖大学）；IT-UBI（IT-UBI机构）；EPFL（洛桑联邦理工学院）
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
School of Computer Science, Nanjing University of Information Science & Technology（南京信息工程大学计算机科学学院）；Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)（江苏省大气环境与装备技术协同创新中心）；School of Software, Nanjing University of Information Science & Technology（南京信息工程大学软件学院）；department of Intelligent Science, Xi’an Jiaotong-Liverpool University（智能科学系，西安交通大学利物浦大学）；Department of Computing Sciences, Tampere University（计算科学系，塔尔基大学）；School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）
Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation
Electronics and Telecommunications Research Institute（电子和电信研究所）；Dept. of Electronics and Informatics, Vrije Universiteit Brussel（布鲁塞尔自由大学电子与信息系）；IMEC（imec公司）；Department of Computer Science, Texas State University（德克萨斯州立大学计算机科学系）；Embedded Computing Lab., Dept. of Computer Science and Statistics, Federal University of Santa Catarina（圣卡塔琳娜联邦大学计算机科学与统计系嵌入式计算实验室）；Licks Attorneys（利克斯律师事务所）；Federal University of Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东理工大学宁波数字孪生研究院）；Instituto Militar de Engenharia-IME, PGEE/PGED（军事工程学院-IME，PGEE/PGED）
Neural video codecs quality assessment dataset and benchmark
AI Center, Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学人工智能中心）；MSU Institute for Artificial Intelligence（莫斯科国立大学人工智能研究所）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）
State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；ByteDance, LarkAI（字节跳动 LarkAI）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）
SoLA-Vision: Fine-grained Layer-wise Linear Softmax Hybrid Attention
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information（北京多模信息超智能安防重点实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Multi-scale Grouped Dense Network for VVC Intra Coding
University of Science and Technology of China（中国科学技术大学）
多功能视频编码（Versatile Video Coding，H.266/VVC）标准在保持相同比特数的情况下，比传统的图像编解码器（如BPG、JPEG等）具有更好的图像质量。在本文中，我们设计的多尺度分组密集网络（MSGDN），以进一步减少压缩伪影相结合的多尺度和分组密集块，这是集成的VVC帧内编码的后处理网络。此外，为了提高压缩图像的主观质量，我们还提出了一个生成对抗网络（MSGDN-GAN），利用我们的MSGDN作为发电机。在验证集上的广泛实验中，我们通过MSE损失训练的MSGDN在低速率轨道中以0.15的比特率使用IMC团队平均产生32.622的PSNR。此外，我们的MSGDN-GAN可以实现更好的主观性能。
BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China（中山大学计算机科学与技术学院，深圳校区）；vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., China（vivo蓝影实验室，vivo移动通信有限公司）
Video compression dataset and benchmark of learning-based video-quality metrics
ISP RAS（俄罗斯科学院信息传输问题研究所）；Research Center for Trusted Artificial Intelligence（可信人工智能研究中心）；MSU Institute for Artificial Intelligence（莫斯科国立大学人工智能研究所）；Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）
RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation
School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）；School of Engineering, Royal Melbourne Institute of Technology（工程学院，皇家墨尔本理工学院）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；School of Telecommunication Engineering, Xidian University（电信工程学院，西安电子科技大学）
Hierarchical Semantic Compression for Consistent Image Semantic Restoration
School of Electronic and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Electronic and Information Engineering, University of Electronic Science and Technology of China（电子科技大学电子信息工程学院）
