Hadi
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EUVIP’25 Tutorial: From Subjective Ratings to Objective Metrics

EUVIP 2025
October 13-16, 2025

Malta

Link

Tutorial speakers:

  • Wei Zhou (Cardiff University)
  • Hadi Amirpour (University of Klagenfurt)

Tutorial description:

As multimedia services like video streaming, video conferencing, virtual reality (VR), and online gaming continue to evolve, ensuring high perceptual visual quality is crucial for enhancing user experience and maintaining competitiveness. However, multimedia content inevitably undergoes various distortions during acquisition, compression, transmission, and storage, leading to quality degradation. Therefore, perceptual visual quality assessment, which evaluates multimedia quality from a human perception perspective, plays a vital role in optimizing user experience in modern communication systems. This tutorial provides a comprehensive overview of perceptual visual quality assessment, covering both subjective methods, where human observers directly rate their experience, and objective methods, where computational models predict perceptual quality based on measurable factors such as bitrate, frame rate, and compression levels. The session also explores quality assessment metrics tailored to different types of multimedia content, including images, videos, VR, point clouds, meshes, and AI-generated media. Furthermore, we discuss challenges posed by diverse multimedia characteristics, complex distortion scenarios, and varying viewing conditions. By the end of this tutorial, attendees will gain a deep understanding of the principles, methodologies, and latest advancements in perceptual visual quality assessment for multimedia communication.

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IEEE ICIP’25: Machine Learning-Based Decoding Energy Modeling for VVC Streaming

Machine Learning-Based Decoding Energy Modeling for VVC Streaming

2025 IEEE International Conference on Image Processing (ICIP)

14-17 September, Anchorage, Alaska, USA

https://2025.ieeeicip.org/

Reza Farahani (AAU Klagenfurt, Austria), Vignesh V Menon (Fraunhofer HHI, Germany), and Christian Timmerer (AAU Klagenfurt, Austria)

Abstract: Efficient video streaming requires jointly optimizing encoding parameters (bitrate, resolution, compression efficiency) and decoding constraints (computational load, energy consumption) to balance quality and power efficiency, particularly for resource-constrained devices. However, hardware heterogeneity, including differences in CPU/GPU architectures, thermal management, and dynamic power scaling, makes absolute energy models unreliable, particularly for predicting decoding consumption. This paper introduces the Relative Decoding Energy Index (RDEI), a metric that normalizes decoding energy consumption against a baseline encoding configuration, eliminating device-specific dependencies to enable cross-platform comparability and guide energy-efficient streaming adaptations. We use a dataset of 1000 video sequences to extract complexity features capturing spatial and temporal variations, employ Versatile Video Coding (VVC) open-source toolchain using VVenC/VVdeC with various resolutions, framerate, encoding preset and quantization parameter (QP) sets, and model RDEI using Random Forest (RF), XGBoost, Linear Regression (LR), and Shallow Neural Networks (NN) for decoding energy prediction. Experimental results demonstrate that RDEI-based predictions provide accurate decoding energy estimates across different hardware, ensuring cross-device comparability in VVC streaming.

Keywords: Video Streaming; Energy Prediction; Versatile Video Coding (VVC); Video Complexity Analysis.

 

 

 

 

 

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