44-Issue 8

Permanent URI for this collection

Now showing 1 - 1 of 1

CGVQM+D: Computer Graphics Video Quality Metric and Dataset
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Jindal, Akshay; Sadaka, Nabil; Thomas, Manu Mathew; Sochenov, Anton; Kaplanyan, Anton; Knoll, Aaron; Peters, Christoph
While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0.78. Additionally, we find that the feature space of pre-trained 3D CNNs aligns strongly with human perception of visual quality. We propose CGVQM, a full-reference video quality metric that significantly outperforms existing metrics while generating both per-pixel error maps and global quality scores. Our dataset and metric implementation is available at https://github.com/IntelLabs/CGVQM.