# CGVQM+D: Computer Graphics Video Quality Metric and Dataset

arXiv: https://arxiv.org/abs/2506.11546 · HTML: https://mlsanity.com/paper/2506.11546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [PyTorch Image Quality: Metrics for Image Quality Assessment](https://mlsanity.com/paper/2208.14818/) (2022)
- [Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator](https://mlsanity.com/paper/1608.07433/) (2016)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [Subjective and Objective Quality Assessment of High Frame Rate Videos](https://mlsanity.com/paper/2007.11634/) (2020)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [A Subjective and Objective Study of Space-Time Subsampled Video Quality](https://mlsanity.com/paper/2102.00088/) (2021)
- [ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution](https://mlsanity.com/paper/2110.09992/) (2021)
- [Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality](https://mlsanity.com/paper/2012.10758/) (2020)
- [A Survey on Perceptually Optimized Video Coding](https://mlsanity.com/paper/2112.12284/) (2021)
- [Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos](https://mlsanity.com/paper/2305.17260/) (2023)
- [NeRF View Synthesis: Subjective Quality Assessment and Objective Metrics Evaluation](https://mlsanity.com/paper/2405.20078/) (2024)
- [User Generated HDR Gaming Video Streaming: Dataset, Codec Comparison and Challenges](https://mlsanity.com/paper/2103.02189/) (2021)

## Built on by (most cited first, 1 of 1)

- [Gaussians on Fire: High-Frequency Reconstruction of Flames](https://mlsanity.com/paper/2511.22459/) (2025)

## Abstract (arXiv)

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.