Image and Video Quality Assessment

Neueste Paper

• Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment

  Sheng Zhao, Weikai Lin, Yuhao Zhu · 1. Oktober 2026

  Judging image quality is not only ecologically relevant to everyday human tasks, but also underpins many machine vision tasks such as image generation. This paper proposes a framework to understand the inherent perceptual space underlying image quality judgment in humans. We propose a multi-dimensio…

• CatSIM: A Categorical Image Similarity Metric

  Geoffrey Z. Thompson, Ranjan Maitra · 25. September 2026

  We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, image or volum…

• Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment

  Andrei Velichko, Petr Boriskov · 24. September 2026

  We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values computed directly from two-dimensional image patches. For each $3\times3$-pixel grayscale patch, SVD is applied directly and the normalized singular-val…

• When Visual Quality Misleads: Intent Recognition under Rendered Avatar Distortions

  Ning-Hsuan Chang, Kai-Siang Ma, Yu-Chih Chen · 24. September 2026

  Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a proxy for communicative success. We test this assumption through a controlled behavioral study of rendered 3D avatars across a pristine condition and fo…

• An Evolutionary Agentic Approach for Open-ended Image Quality Perception

  Zhenchen Tang, Bo Peng, Zichuan Wang, Songlin Yang, Leilei Cao, Fengjie Zhu, Jing Dong · 22. September 2026

  Generative models are rapidly expanding image quality assessment (IQA) beyond traditional fidelity factors to emerging dimensions such as physical plausibility and text-rendering correctness. However, existing IQA models rely on fixed definitions and heavy supervision, making them difficult to exten…

• Scientific Image Quality Assessment via Multi-modal Retrieval-Augmented Generation

  Yinuo Zhang, Bingshuo Liu, Zhiying Tu, Dianhui Chu, Qingbin Liu, Xi Chen, Jiang Bian, Xiaoyan Yu, Dianbo Sui · 18. September 2026

  This paper proposes a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, designed to simultaneously address both the understanding track (SIQA-U) and the scoring track (SIQA-S) of the SIQA challenge. We construct a multimodal index that integrates textual semanti…

• High-Fidelity Video Quality Assessment with VQA-Specific Saliency

  Hakan Emre Gedik, Shashank Gupta, Alan Bovik · 16. September 2026

  No-reference video quality assessment (NR VQA) has recently seen promising progress with deep learning. However, video data is inherently large, and processing them with deep models incurs high computational cost. This challenge is particularly acute in VQA, where preserving original-resolution cues…

• Bridging the Perceptual Gap: Residual-Enhanced Downscaling and Manifold-Aware Perception Alignment Adaptation for NR-IQA

  Yu Li, Zhengran Shen, Yachun Mi, Puchao Zhou, Shaohui Liu · 16. September 2026

  Leveraging Large Vision-Language Models like CLIP has recently set new benchmarks for No-Reference Image Quality Assessment (NR-IQA). However, the contrastive pretraining of CLIP inherently prioritizes semantic invariance, which often suppresses subtle perceptual signals, a phenomenon we term percep…

• Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework

  Yan Zhong, Gefei Chen, Qiufang Ma, Zhen Wang, Zhiwei Fan, Lei Shi, Tingting Jiang · 3. September 2026

  Image enhancement and restoration have become standard back-end operations on short-video and social media platforms to boost UGC visual experience. Yet these processes inevitably introduce visual anomalies--especially in faces, texts, and textures--that directly undermine perceptual fidelity and vi…

• VGA-BenchV2: An Expanded Unified Benchmark and Multi-Model Framework for Evaluating Video Aesthetics and Generation Quality

  Longteng Jiang, DanDan Zheng, Qianqian Qiao, Heng Huang, Huaye Wang, Yihang Bo, Bao Peng, Jingdong Chen, Jun Zhou, Xin Jin · 27. August 2026

  We introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value. Built upon VGA-Bench, VGA-BenchV2 preserves the original fine-grained taxonomy with two primary dimensions-Aesthetic and Generat…

• Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment

  Baoliang Chen, Qing Lin, Sijie Mai · 26. August 2026

  AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that are often treated as independent in existing AIGIQA models. However, by re-examining human ratings, we uncover a previously overlooked phenomenon: the…

• Towards Bitstream-corrupted Harsh Visual Understanding: Through Bitstream Language Modeling as Robust Semantic Priors

  Chaoran Huang, Fangcheng Li, Tianyi Liu, Wenyang Liu, Kejun Wu · 25. August 2026

  Bitstream-corrupted Harsh Visual Understanding (BcHVU) aims to understand harshly degraded videos originally decoded from a severely corrupted bitstream in real-world multimedia communication. The ill-posed nature of BcHVU poses a major challenge for existing vision models, as even subtle bitstream …

• CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation

  Yunhe Li, Likun Wu, Sijing Wu, Xinyu Tian, Huiyu Duan, Yixuan Gao, Yunhao Li, Guangtao Zhai · 20. August 2026

  Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-defined camera trajectories. However, existing video quality assessment (VQA) methods are mainly developed for natural videos and fail to capture the uniq…

• PCQA-R1: Advancing Generalized 3D Point Cloud Quality Assessment with Reinforcement Learning

  Kangning Ye, Yunhao Li, Sijing Wu, Yucheng Zhu, Guangtao Zhai · 20. August 2026

  No-reference point cloud quality assessment (PCQA) has been an active topic in recent years and is used to measure and optimize the visual experience of point clouds. However, large multimodal models (LMMs) have rarely been explored in this area. Previous LMM-based methods mainly rely on supervised …

• MR-IQA-2: Faithful Image Quality Reflection via Fine-Grained Credit Assignment

  Yuan li, Youyuan Lin, Chenhui Chu, Shin'ya Nishida · 20. August 2026

  Multimodal large language models (MLLMs) have shown strong potential for image quality assessment (IQA) by improving consistency between quality ratings and their underlying reasoning. However, most approaches supervise reasoning through human-provided ratings and rarely examine whether it faithfull…

• A Subjective Study on a New Sharpness Informed Class of Metrics

  Uditangshu Aurangabadkar, Vibhoothi Vibhoothi, Darren Ramsook, Anil Kokaram · 17. August 2026

  Perceptual loss functions in Deep Neural Network (DNN) deblurring architectures improve the overall quality of restored images. However, few focus on explicitly targeting sharpness in the restorations. We conduct a subjective study of models trained with and without losses which explicitly target sh…

• Towards Adaptive Super-Resolution and Quality Assessment via Test-Time Adaptation

  Ajeet Kumar Verma · 11. August 2026

  This paper presents doctoral research on adaptive video super-resolution and perceptual quality modeling under real-world conditions. Existing video super-resolution (VSR) methods struggle to generalize under unknown degradations arising from heterogeneous devices, codecs, and network environments. …

• Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation

  Yifei Xue, Yuanchen Fei, Hao Zhang, Chenzhi Nie, Tie ji, Yizhen Lao · 11. August 2026

  Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization. Existing studies assess video quality through visual harmony, vi…

• Visual Distortion Detection in UGC Images Using Large Multimodal Models

  Ziheng Jia, Yingji Liang, Jiaying Qian, Xiongkuo Min · 11. August 2026

  The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA). Existing approaches based on large multimodal models (LMMs) predominantly rely on text-driven supervised fine-tuning (SFT). However, this training paradigm exhibit…

• 3DGSI-Assessor: A Large-Scale Dataset and An LMM-based Method for 3D Gaussian Splatting Image Quality Assessment

  Yuke Xing, Jiarui Wang, William Gordon, Zhu Li, Guangtao Zhai, Yiling Xu · 5. August 2026

  3D Gaussian Splatting (3DGS) has become a dominant representation for real-time novel view synthesis (NVS), yet its storage footprint makes compression indispensable for practical deployment. 3DGS training and compression introduce representation-specific distortions such as floating artifacts and s…

• Estimating SSIM from MSE for DCT-Based Compressed Images

  Luc Trudeau, Maria G. Martini · 4. August 2026

  Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming systems. This article shows that, in the context of Discrete Cosine Transform ( DCT)-based compressed images, Structural Similarity Index ( SSIM ) can be a…

• Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

  Hakan Emre Gedik, Shashank Gupta, Alan Bovik · 4. August 2026

  No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly train…

• FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration

  Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe, Jung-Hsien Chiang · 4. August 2026

  Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward co…

• Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features

  Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub · 4. August 2026

  We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on source-domain-specific decision boundaries. The proposed approach supports both zero-shot and few-shot adaptation without req…

• What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration

  Cencen Liu, Wen Yin, Dongyang Zhang, Dongmin Li, Shan Zhao, Bing Su, Tao He, Jielei Wang, Guoming Lu · 31. Juli 2026

  All-in-one image restoration aims to handle diverse degradations within a unified framework. Existing methods commonly encode heterogeneous degradation conditions in a shared latent space, where degradation-related cues and scene content can remain entangled. We characterize the resulting challenge …