CamWorldQA introduces the first benchmark for assessing the perceptual quality of camera‑controlled world video generation, featuring 720 videos generated by six methods from 20 source videos across six camera trajectories, each scored by human raters. The paper also presents CWQA, a no‑reference quality assessment network that combines spatial, temporal motion, and optical flow features to predict quality scores. Experiments show CWQA outperforms existing VQA methods on the CamWorldQA dataset.
The paper presents a real‑time on‑device system for editing the emotional intensity of visual content. Using a MobileNetV4 backbone with FiLM‑based conditioning, the model predicts parameters for differentiable global transformations in a single 3.7 ms forward pass, replacing 80‑second per‑image optimization. A user study with 54 participants showed reduced viewer arousal and higher perceived quality compared to a grayscale filter, and the system runs at 60 fps on a Samsung Galaxy S23.
By Musa Rochi, Marcel Schubert, Christoph Gebhardt
arXiv:2608.29331v1 Announce Type: new
Abstract: Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved....
By Nikolay Safonov, Nikita Gornostaev, Alexandra Dubonos, Dmitriy Vatolin
arXiv:2610.17661v2 Announce Type: replace
Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...
By Shuhan Xia, Yu Chen, Xiang Wang, Shuai Li, Peipie Li, Zekun Li
The paper introduces GS-PQM, a full-reference quality metric designed for compressed Gaussian Splatting (GS) models. Unlike traditional metrics that depend on rendered images or videos, GS-PQM evaluates perceptual quality directly in the GS parameter domain using a Support Vector Regression model. Experiments demonstrate that GS-PQM outperforms 25 existing image, video, and point‑cloud quality metrics, offering a more accurate and computationally efficient alternative to rendering‑based assessment.
By Pedro Martin, Ant\'onio Rodrigues, Jo\~ao Ascenso, Maria Paula Queluz
arXiv:2506.19445v5 Announce Type: replace
Abstract: Motion blur remains one of the most common and visually disruptive degradations in real-world smartphone imaging, yet existing deblurring benchmark...
By Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi, Sudipto Das Sukanto, Afia Lubaina, Md. Mosaddek Khan
LLaVA‑Assessor is a unified large multi‑modal model (LMM) designed for visual quality assessment, combining image and video inputs. It introduces a two‑task framework—quality interpretation and quality scoring—supported by an adaptive architecture, a rigorous human‑annotated dataset, and a machine‑synthesized data expansion pipeline. The model employs a prompt‑disentanglement strategy to stabilize multi‑task training and achieves strong performance across 11 quality scoring test sets and 4 interpretation benchmarks.
By Ziheng Jia, Zicheng Zhang, Jiaying Qian, Guangtao Zhai, Xiongkuo Min
The paper proposes an explicit pixel‑level regional representation for video quality assessment called a Quality‑Relevance Heatmap (QRH), which fuses localized degradation evidence with perceptual priors guided by a vision‑language model, all without pixel‑level annotations. Two deterministic fragment‑sampling strategies are introduced: distribution‑preserving sampling approximates the original QRH‑score distribution without retraining, while quality‑aware sampling focuses on high‑QRH regions and is used with fine‑tuning. Experiments on the DOVER model across multiple benchmarks show that distribution‑preserving sampling closely matches predictions from random sampling, and quality‑aware sampling improves most evaluated metrics, demonstrating the utility of explicit regional representations for downstream VQA tasks.
By Fuyi Yang, Lei Yang, Andrew Liu, Chenyu Wei, Xiaoyi Guo, Ming Kai Hsu
MoI-Bench is a new benchmark designed for on‑device image generation and editing, featuring 64 subtasks and 1150 samples that cover 16 operations and eight visual target categories. It introduces MoI‑Eval, a task‑specific evaluation framework that assesses instruction following, visual quality, and detail preservation, and aggregates results into a two‑dimensional Quality‑Deployability map. The benchmark reveals that current on‑device models excel at efficient generation or editing but need improved editing quality and broader task coverage, especially for text replacement and identity preservation.
By Kailai Feng, Haoxiang Rao, Yuxiang Wei, Bo Chen, Junsong Li, Yang Pan, Chengkun Cai, Jagmohan Chauhan
The paper introduces RateAR, a benchmark of AR images and videos that captures perceptual factors such as object placement, scale, and shadow consistency. Eleven commercial vision‑language models (VLMs) are evaluated on this benchmark, with VLM‑based quality predictions showing strong correlation with human judgments (Spearman’s ρ up to 0.8695). Using contextual prompting, the authors build an automated AR content adjustment system that, in a 21‑participant study, improved placement and size coherence of virtual content for over 90% of users.
By Elias Rotondo (Duke University), Lin Duan (Duke University), Yanming Xiu (Duke University), Sangjun Eom (Duke University), Conrad Li (Duke University), Maria Gorlatova (Duke University)
arXiv:2610.34854v1 Announce Type: new
Abstract: When zooming between dual cameras on smartphones, prominent discontinuities in both geometric perspective and color rendition inevitably degrade the vi...
By Junjie Fan, Zhilu Zhang, Renlong Wu, Dazhi Zhang, Wangmeng Zuo
VOR-Bench is a new benchmark for video object removal that addresses shortcomings in current evaluation methods by providing a dataset with paired edited videos and graffiti masks, a realistic motion-capable paired-video acquisition framework (rMPAF), and a perception-driven scoring model (VOR-MDSM). The dataset includes diverse data from model-generated, tool-rendered, and camera-captured sources, ensuring robust real-world assessment. Experiments show that VOR-Bench’s evaluation results correlate strongly (ρ > 0.9) with human subjective judgments, bridging the gap between traditional metrics and human preference.
By Haonan Huang, Tianrui Qiu, Xianghao Zang, Yinan Du, Zhixiang He, Chi Zhang, Hao Sun, Zhongjiang He, Tianwei Cao, Xuchong Zhang, Hongbin Sun, Kongming Liang, Zhanyu Ma