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Hadi

ICCV 2025 Workshop: Visual Quality Assessment Competition

Visual Quality Assessment Competition

VQualA

co-located with ICCV 2025

https://vquala.github.io/

ICCV 2025 Workshop: Visual Quality Assessment Competition | ATHENA Christian Doppler (CD) Laboratory

VQualA Logo

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

 

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Journal article accepted: ACM TOMM: HTTP Adaptive Streaming: A Review on Current Advances and Future Challenges

ACM Transactions on Multimedia Computing, Communications, and Applications

 

Christian Timmerer (AAU, AT), Hadi Amirpour (AAU, AT), Farzad Tashtarian (AAU, AT), Samira Afzal (AAU, AT), Amr Rizk (Leibniz University Hannover, DE), Michael Zink (University of Massachusetts Amherst, US), and Hermann Hellwagner (AAU, AT)

Abstract: Video streaming has evolved from push-based, broad-/multicasting approaches with dedicated hard-/software infrastructures to pull-based unicast schemes utilizing existing Web-based infrastructure to allow for better scalability. In this article, we provide an overview of the foundational principles of HTTP adaptive streaming (HAS), from video encoding to end user consumption, while focusing on the key advancements in adaptive bitrate algorithms, quality of experience (QoE), and energy efficiency. Furthermore, the article highlights the ongoing challenges of optimizing network infrastructure, minimizing latency, and managing the environmental impact of video streaming. Finally, future directions for HAS, including immersive media streaming and neural network-based video codecs, are discussed, positioning HAS at the forefront of next-generation video delivery technologies.

Keywords: HTTP Adaptive Streaming, HAS, DASH, Video Coding, Video Delivery, Video Consumption, Quality of Experience, QoE

 

https://athena.itec.aau.at/2025/03/acm-tomm-http-adaptive-streaming-a-review-on-current-advances-and-future-challenges/

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Paper accepted: ICME 2025: Neural Representations for Scalable Video Coding

Neural Representations for Scalable Video Coding

IEEE International Conference on Multimedia & Expo (ICME) 2025

 

Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria),  and Christian Timmerer (AAU, Austria)

 

Abstract: Scalable video coding encodes a video stream into multiple layers so that it can be decoded at different levels of quality/resolution, depending on the device’s capabilities or the available network bandwidth. Recent advances in implicit neural representation (INR)-based video codecs have shown competitive compression performance to both traditional and other learning-based methods. In INR approaches, a neural network is trained to overfit a video sequence, and its parameters are compressed to create a compact representation of the video content. While they achieve promising results, existing INR-based codecs require training separate networks for each resolution/quality of a video, making them challenging for scalable compression. In this paper, we propose Neural representations for Scalable Video Coding (NSVC) that encodes multi-resolution/-quality videos into a single neural network comprising multiple layers. The base layer (BL) of the neural network encodes video streams with the lowest resolution/quality. Enhancement layers (ELs) encode additional information that can be used to reconstruct a higher resolution/quality video during decoding using the BL as a starting point. This multi-layered structure allows the scalable bitstream to be truncated to adapt to the client’s bandwidth conditions or computational decoding requirements. Experimental results show that NSVC outperforms AVC’s Scalable Video Coding (SVC) extension and surpasses HEVC’s scalable extension (SHVC) in terms of VMAF. Additionally, NSVC achieves comparable decoding speeds at high resolutions/qualities.

 

ICME 2025: Neural Representations for Scalable Video Coding | ATHENA Christian Doppler (CD) Laboratory