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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Mon, 20 Oct 2025 00:24:25 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"68f58139-646b" expires: Sun, 28 Dec 2025 14:57:19 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 5D74:328FD3:7BE97C:8ADCE5:695142F6 accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 14:47:19 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210074-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766933239.894263,VS0,VE205 vary: Accept-Encoding x-fastly-request-id: a1b94fb3fea4a17c79e45ddb3c7fef9a5cd2d866 content-length: 7058 VQualA: Visual Quality Assessment Competition

Visual Quality Assessment Competition (VQualA)

ICCV 2025

13:00-17:00 HST at 303B, 19 October 2025

Call for Papers

Visual quality assessment plays a crucial role in computer vision, serving as a fundamental step in tasks such as image quality assessment (IQA), image super-resolution, document image enhancement, and video restoration. Traditional visual quality assessment techniques often rely on scalar metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), which, while effective in certain contexts, fall short in capturing the perceptual quality experienced by human observers. This gap emphasizes the need for more perceptually aligned and comprehensive evaluation methods that can adapt to the growing demands of applications such as medical imaging, satellite remote sensing, immersive media, and document processing. In recent years, advancements in deep learning, generative models, and multimodal large language models (MLLMs) have opened up new avenues for visual quality assessment. These models offer capabilities that extend beyond traditional scalar metrics, enabling more nuanced assessments through natural language explanations, open-ended visual comparisons, and enhanced context awareness. With these innovations, VQA is evolving to better reflect human perceptual judgments, making it a critical enabler for next-generation computer vision applications.

The VQualA Workshop aims to bring together researchers and practitioners from academia and industry to discuss and explore the latest trends, challenges, and innovations in visual quality assessment. We welcome original research contributions addressing, but not limited to, the following topics:

Image and video quality assessment
Perceptual quality assessment techniques
Multi-modal quality evaluation (image, video, text)
Visual quality assessment for immersive media (VR/AR)
Document image enhancement and quality analysis
Quality assessment under adverse conditions (low light, weather distortions, motion blur)
Robust quality metrics for medical and satellite imaging
Perceptual-driven image and video super-resolution
Visual quality in restoration tasks (denoising, deblurring, upsampling)
Human-centric visual quality assessment
Learning-based quality assessment models (CNNs, Transformers, MLLMs)
Cross-domain visual quality adaptation
Benchmarking and datasets for perceptual quality evaluation
Integration of large language models for quality explanation and assessment
Open-ended comparative assessments with natural language reasoning
Emerging applications of VQA in autonomous driving, surveillance, and smart cities

Submission Details
Papers will be peer-reviewed and comply with the ICCV 2025 proceedings style, format and length. The camera-ready deadline aligns with the main conference. Accepted papers must be registered and presented to ensure their inclusion in the IEEE Xplore Library. For details, refer to the ICCV 2025 Author Guidelines.

Submit You Paper

Challenges

VQualA 2025 Challenges

One needs to check the corresponding competition page(s) in order to learn more about and to register to access the data and participate in the challenge(s) of interest.