# C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network

arXiv: https://arxiv.org/abs/1910.13646 · HTML: https://mlsanity.com/paper/1910.13646/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 22 of 22)

- [FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation](https://mlsanity.com/paper/2207.08119/) (2022)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [A Perceptual Quality Metric for Video Frame Interpolation](https://mlsanity.com/paper/2210.01879/) (2022)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [BVI-VFI: A Video Quality Database for Video Frame Interpolation](https://mlsanity.com/paper/2210.00823/) (2022)
- [CompressedVQA-HDR: Generalized Full-reference and No-reference Quality Assessment Models for Compressed High Dynamic Range Videos](https://mlsanity.com/paper/2507.11900/) (2025)
- [Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis](https://mlsanity.com/paper/2312.11024/) (2023)
- [Perceptual Quality Assessment of Virtual Reality Videos in the Wild](https://mlsanity.com/paper/2206.08751/) (2022)
- [LSTM-based Video Quality Prediction Accounting for Temporal Distortions in Videoconferencing Calls](https://mlsanity.com/paper/2303.12761/) (2023)
- [Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs](https://mlsanity.com/paper/2309.00769/) (2023)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)
- [MVAD: A Multiple Visual Artifact Detector for Video Streaming](https://mlsanity.com/paper/2406.00212/) (2024)
- [BVI-UGC: A Video Quality Database for User-Generated Content Transcoding](https://mlsanity.com/paper/2408.07171/) (2024)
- [Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection](https://mlsanity.com/paper/2210.14153/) (2022)
- [RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment](https://mlsanity.com/paper/2312.08864/) (2023)
- [DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment](https://mlsanity.com/paper/2504.11733/) (2025)
- [Study of the effect of Sharpness on Blind Video Quality Assessment](https://mlsanity.com/paper/2404.05764/) (2024)
- [Towards Unified Video Quality Assessment](https://mlsanity.com/paper/2512.02224/) (2025)
- [A Mamba-based Perceptual Loss Function for Learning-based UGC Transcoding](https://mlsanity.com/paper/2603.25566/) (2026)
- [LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models](https://mlsanity.com/paper/2607.01086/) (2026)
- [GS-PQM: A Parameter-Domain Quality Metric for Compressed Gaussian Splatting](https://mlsanity.com/paper/2610.00195/) (2026)

## Abstract (arXiv)

Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality exhibits different characteristics from static image quality due to the existence of temporal masking effects. In this paper, we present a novel architecture, namely C3DVQA, that uses Convolutional Neural Network with 3D kernels (C3D) for full-reference VQA task. C3DVQA combines feature learning and score pooling into one spatiotemporal feature learning process. We use 2D convolutional layers to extract spatial features and 3D convolutional layers to learn spatiotemporal features. We empirically found that 3D convolutional layers are capable to capture temporal masking effects of videos. We evaluated the proposed method on the LIVE and CSIQ datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance.