Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision

Video quality assessment (VQA) is essential for quantifying perceptual quality in various video processing workflows, spanning from camera capture systems to over-the-top streaming platforms. While recent supervised VQA models have made substantial progress, the reliance on manually annotated datasets -- a process that is labor-intensive, costly, and difficult to scale up -- has hindered further optimization of their generalization to unseen video content and distortions. To bridge this gap, we introduce a self-supervised learning framework for VQA to learn quality assessment capabilities from large-scale, unlabeled web videos. Our approach leverages a \textbf{learning-to-rank} paradigm to train a large multimodal model (LMM) on video pairs automatically labeled via two manners, including quality pseudo-labeling by existing VQA models and relative quality ranking based on synthetic distortion simulations. Furthermore, we introduce a novel \textbf{iterative self-improvement training strategy}, where the trained model acts an improved annotator to iteratively refine the annotation quality of training data. By training on a dataset $10\times$ larger than the existing VQA benchmarks, our model: (1) achieves zero-shot performance on in-domain VQA benchmarks that matches or surpasses supervised models; (2) demonstrates superior out-of-distribution (OOD) generalization across diverse video content and distortions; and (3) sets a new state-of-the-art when fine-tuned on human-labeled datasets. Extensive experimental results validate the effectiveness of our self-supervised approach in training generalized VQA models. The datasets and code will be publicly released to facilitate future research.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor

Video Quality Assessment (VQA) aims to evaluate video quality based on perceptual distortions and human preferences. Despite the promising performance of existing methods using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), they often struggle to align closely with human perceptions, particularly in diverse real-world scenarios. This challenge is exacerbated by the limited scale and diversity of available datasets. To address this limitation, we introduce a novel VQA framework, DiffVQA, which harnesses the robust generalization capabilities of diffusion models pre-trained on extensive datasets. Our framework adapts these models to reconstruct identical input frames through a control module. The adapted diffusion model is then used to extract semantic and distortion features from a resizing branch and a cropping branch, respectively. To enhance the model's ability to handle long-term temporal dynamics, a parallel Mamba module is introduced, which extracts temporal coherence augmented features that are merged with the diffusion features to predict the final score. Experiments across multiple datasets demonstrate DiffVQA's superior performance on intra-dataset evaluations and its exceptional generalization across datasets. These results confirm that leveraging a diffusion model as a feature extractor can offer enhanced VQA performance compared to CNN and ViT backbones.

Securing Immersive 360 Video Streams through Attribute-Based Selective Encryption

Delivering high-quality, secure 360{\deg} video content introduces unique challenges, primarily due to the high bitrates and interactive demands of immersive media. Traditional HTTPS-based methods, although widely used, face limitations in computational efficiency and scalability when securing these high-resolution streams. To address these issues, this paper proposes a novel framework integrating Attribute-Based Encryption (ABE) with selective encryption techniques tailored specifically for tiled 360{\deg} video streaming. Our approach employs selective encryption of frames at varying levels to reduce computational overhead while ensuring robust protection against unauthorized access. Moreover, we explore viewport-adaptive encryption, dynamically encrypting more frames within tiles occupying larger portions of the viewer's field of view. This targeted method significantly enhances security in critical viewing areas without unnecessary overhead in peripheral regions. We deploy and evaluate our proposed approach using the CloudLab testbed, comparing its performance against traditional HTTPS streaming. Experimental results demonstrate that our ABE-based model achieves reduced computational load on intermediate caches, improves cache hit rates, and maintains comparable visual quality to HTTPS, as assessed by Video Multimethod Assessment Fusion (VMAF).

NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results

This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground truth, which suffer from real-world degradations such as noise, blur, faded colors, compression artifacts, etc. The goal of the participants was to develop an algorithm capable of improving the visual quality of such videos. Given the widespread use of UGC on short-form video platforms, this task holds substantial practical importance. The evaluation was based on subjective quality assessment in crowdsourcing, obtaining votes from over 8000 assessors. The challenge attracted more than 25 teams submitting solutions, 7 of which passed the final phase with source code verification. The outcomes may provide insights into the state-of-the-art in UGC video enhancement and highlight emerging trends and effective strategies in this evolving research area. All data, including the processed videos and subjective comparison votes and scores, is made publicly available at https://github.com/msu-video-group/NTIRE25_UGC_Video_Enhancement.

LMME3DHF: Benchmarking and Evaluating Multimodal 3D Human Face Generation with LMMs

The rapid advancement in generative artificial intelligence have enabled the creation of 3D human faces (HFs) for applications including media production, virtual reality, security, healthcare, and game development, etc. However, assessing the quality and realism of these AI-generated 3D human faces remains a significant challenge due to the subjective nature of human perception and innate perceptual sensitivity to facial features. To this end, we conduct a comprehensive study on the quality assessment of AI-generated 3D human faces. We first introduce Gen3DHF, a large-scale benchmark comprising 2,000 videos of AI-Generated 3D Human Faces along with 4,000 Mean Opinion Scores (MOS) collected across two dimensions, i.e., quality and authenticity, 2,000 distortion-aware saliency maps and distortion descriptions. Based on Gen3DHF, we propose LMME3DHF, a Large Multimodal Model (LMM)-based metric for Evaluating 3DHF capable of quality and authenticity score prediction, distortion-aware visual question answering, and distortion-aware saliency prediction. Experimental results show that LMME3DHF achieves state-of-the-art performance, surpassing existing methods in both accurately predicting quality scores for AI-generated 3D human faces and effectively identifying distortion-aware salient regions and distortion types, while maintaining strong alignment with human perceptual judgments. Both the Gen3DHF database and the LMME3DHF will be released upon the publication.

Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. Therefore, they are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, $2.34 \pm 0.61$ h, $4.50 \pm 1.46$ h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63\%) in identifying live oysters compared to experts (74\%) and non-experts (75\%) alike. Image quality was an important factor in determining the accuracy of the model and the annotators. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs. dead oysters.

Automated ARAT Scoring Using Multimodal Video Analysis, Multi-View Fusion, and Hierarchical Bayesian Models: A Clinician Study

Manual scoring of the Action Research Arm Test (ARAT) for upper extremity assessment in stroke rehabilitation is time-intensive and variable. We propose an automated ARAT scoring system integrating multimodal video analysis with SlowFast, I3D, and Transformer-based models using OpenPose keypoints and object locations. Our approach employs multi-view data (ipsilateral, contralateral, and top perspectives), applying early and late fusion to combine features across views and models. Hierarchical Bayesian Models (HBMs) infer movement quality components, enhancing interpretability. A clinician dashboard displays task scores, execution times, and quality assessments. We conducted a study with five clinicians who reviewed 500 video ratings generated by our system, providing feedback on its accuracy and usability. Evaluated on a stroke rehabilitation dataset, our framework achieves 89.0% validation accuracy with late fusion, with HBMs aligning closely with manual assessments. This work advances automated rehabilitation by offering a scalable, interpretable solution with clinical validation.

RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video

Multimodal Large Language Models (MLLMs) increasingly excel at perception, understanding, and reasoning. However, current benchmarks inadequately evaluate their ability to perform these tasks continuously in dynamic, real-world environments. To bridge this gap, we introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis. RTV-Bench uses three key principles: (1) Multi-Timestamp Question Answering (MTQA), where answers evolve with scene changes; (2) Hierarchical Question Structure, combining basic and advanced queries; and (3) Multi-dimensional Evaluation, assessing the ability of continuous perception, understanding, and reasoning. RTV-Bench contains 552 diverse videos (167.2 hours) and 4,631 high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost RTV-Bench performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs. Our benchmark toolkit is available at: https://github.com/LJungang/RTV-Bench.

LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning

Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators face limitations in scalability, context capture, or bias. In this paper, we introduce LecEval, an automated metric grounded in Mayer's Cognitive Theory of Multimedia Learning, to evaluate multimodal knowledge acquisition in slide-based learning. LecEval assesses effectiveness using four rubrics: Content Relevance (CR), Expressive Clarity (EC), Logical Structure (LS), and Audience Engagement (AE). We curate a large-scale dataset of over 2,000 slides from more than 50 online course videos, annotated with fine-grained human ratings across these rubrics. A model trained on this dataset demonstrates superior accuracy and adaptability compared to existing metrics, bridging the gap between automated and human assessments. We release our dataset and toolkits at https://github.com/JoylimJY/LecEval.

Contactless pulse rate assessment: Results and insights for application in driving simulator

Camera-based monitoring of Pulse Rate (PR) enables continuous and unobtrusive assessment of driver's state, allowing estimation of fatigue or stress that could impact traffic safety. Commonly used wearable Photoplethysmography (PPG) sensors, while effective, suffer from motion artifacts and user discomfort. This study explores the feasibility of non-contact PR assessment using facial video recordings captured by a Red, Green, and Blue (RGB) camera in a driving simulation environment. The proposed approach detects subtle skin color variations due to blood flow and compares extracted PR values against reference measurements from a wearable wristband Empatica E4. We evaluate the impact of Eulerian Video Magnification (EVM) on signal quality and assess statistical differences in PR between age groups. Data obtained from 80 recordings from 64 healthy subjects covering a PR range of 45-160 bpm are analyzed, and signal extraction accuracy is quantified using metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show that EVM slightly improves PR estimation accuracy, reducing MAE from 6.48 bpm to 5.04 bpm and RMSE from 7.84 bpm to 6.38 bpm. A statistically significant difference is found between older and younger groups with both video-based and ground truth evaluation procedures. Additionally, we discuss Empatica E4 bias and its potential impact on the overall assessment of contact measurements. Altogether the findings demonstrate the feasibility of camera-based PR monitoring in dynamic environments and its potential integration into driving simulators for real-time physiological assessment.