5th Workshop on Image/Video/Audio Quality Assessment in Computer Vision, VLM and Diffusion Model

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5th Workshop on Image/Video/Audio Quality Assessment in Computer Vision, VLM and Diffusion Model

Important Dates/Links:

• Submission Deadline: 12/7/2025, 23:59 GMT
• Submission Link: OpenReviewLink
• Acceptance Notification Deadline: 1/1/2026
• Camera Ready Papers Submission Deadline: 1/10/26 07:59 AM UTC
• Workshop Day: 3/6/2026 or 3/7/2026 (TBD)
• Workshop Website: https://wacv2026-image-quality-workshop.github.io/
• Authors of accepted papers are required to present their work live, either in-person or remote. If pre-recorded videos are used, the authors are required to join the meeting online to do Q&A live.
• Accepted papers will be published in the WACV 2026 proceedings.
• Depending on the number of submissions we receive, author(s) of submitted papers may be required to review some papers.

Description:

Image, video, and audio quality significantly impacts machine learning and computer vision systems, yet remains underexplored by the broader research community. Real-world applications—from streaming services and autonomous vehicles to cashier-less stores and generative AI—critically depend on robust quality assessment and improvement techniques. Despite their importance, most visual learning systems assume high-quality inputs, while in reality, artifacts from capture, compression, transmission, and rendering processes can severely degrade performance and user experience.

This workshop is particularly timely given the explosive growth of generative AI, which introduces new challenges in quality assessment for both inputs and outputs. By bringing together researchers from industry and academia, we aim to systematically investigate how quality issues affect various visual learning tasks and develop innovative assessment and mitigation techniques. Building on the success of our previous workshops at WACV(2022-2025), we expect to stimulate new research directions and attract more talent to this critical field, ultimately improving the robustness and reliability of computer vision applications across industries.

Topics:

This workshop addresses topics related to image/video/audio quality assessment in machine learning, computer vision, VLM, Diffusion Model, and other types of generative AIs. The topics include, but are not limited to:

• Impact of image/video/audio quality in traditional machine learning and computer vision use cases such as object detection, segmentation, tracking, and recognition;
• Analyze, model and learn the quality impact from image/video/audio acquisition, compression, transcoding, transmission, decoding, rendering, and/or display;
• Techniques used to improve image/video/audio quality in terms of
  • brightening, color adjustment, sharpening, inpainting, deblurring, denoising, de-hazing, de-raining, demosaicing,
  • removing artifacts such as shadows, glare, and reflections, etc.,
  • resolution, frame rate, color gamut, dynamic range (SDR vs. HDR), etc.,
  • noise/echo cancellation, speech enhancement, etc.;
• Novel image/video/audio quality assessment methodologies: full reference, reduced-reference, and non-reference;
• Impact of image/video/audio quality in multi-modal use cases;
• Evaluate image/video/audio quality produced by generative AI;
• Evaluate the hallucination effects in image and video super-resolution and restoration using diffusion models;
• Techniques to measure the quality consistency across different types of content in video (such as ads, movies, streamed content, etc.);
• Datasets, statistics, and theory of image/video/audio quality;
• Research, applications and system development of the above.