# BVI-DVC: A Training Database for Deep Video Compression

arXiv: https://arxiv.org/abs/2003.13552 · HTML: https://mlsanity.com/paper/2003.13552/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 30 of 49)

- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [LDMVFI: Video Frame Interpolation with Latent Diffusion Models](https://mlsanity.com/paper/2303.09508/) (2023)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation](https://mlsanity.com/paper/2111.15483/) (2021)
- [BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation](https://mlsanity.com/paper/2304.02225/) (2023)
- [Offline and Online Optical Flow Enhancement for Deep Video Compression](https://mlsanity.com/paper/2307.05092/) (2023)
- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [Motion-aware Latent Diffusion Models for Video Frame Interpolation](https://mlsanity.com/paper/2404.13534/) (2024)
- [Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN](https://mlsanity.com/paper/2202.07731/) (2022)
- [ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation](https://mlsanity.com/paper/2302.08455/) (2023)
- [In-Loop Filtering via Trained Look-Up Tables](https://mlsanity.com/paper/2407.10926/) (2024)
- [JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation](https://mlsanity.com/paper/2206.04231/) (2022)
- [AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content](https://mlsanity.com/paper/2409.17256/) (2024)
- [RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content](https://mlsanity.com/paper/2411.13362/) (2024)
- [Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning](https://mlsanity.com/paper/2206.04877/) (2022)
- [Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding](https://mlsanity.com/paper/2106.08936/) (2021)
- [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)
- [Lightweight Video Denoising Using a Classic Bayesian Backbone](https://mlsanity.com/paper/2408.03904/) (2024)
- [BVI-UGC: A Video Quality Database for User-Generated Content Transcoding](https://mlsanity.com/paper/2408.07171/) (2024)
- [Fast-OMRA: Fast Online Motion Resolution Adaptation for Neural B-Frame Coding](https://mlsanity.com/paper/2410.21763/) (2024)
- [Deep Learning-Based Intra Mode Derivation for Versatile Video Coding](https://mlsanity.com/paper/2204.04059/) (2022)
- [RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment](https://mlsanity.com/paper/2312.08864/) (2023)
- [NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines](https://mlsanity.com/paper/2401.10761/) (2024)
- [Joint Reference Frame Synthesis and Post Filter Enhancement for Versatile Video Coding](https://mlsanity.com/paper/2404.18058/) (2024)
- [DaBiT: Depth and Blur informed Transformer for Video Focal Deblurring](https://mlsanity.com/paper/2407.01230/) (2024)
- [Partition Map-Based Fast Block Partitioning for VVC Inter Coding](https://mlsanity.com/paper/2504.18398/) (2025)
- [In-Loop Filtering Using Learned Look-Up Tables for Video Coding](https://mlsanity.com/paper/2509.09494/) (2025)
- [Leveraging Compression to Construct Transferable Bitrate Ladders](https://mlsanity.com/paper/2512.12952/) (2025)

## Abstract (arXiv)

Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this paper, a new extensive and representative video database, BVI-DVC, is presented for training CNN-based video compression systems, with specific emphasis on machine learning tools that enhance conventional coding architectures, including spatial resolution and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that this database produces significant improvements in terms of coding gains over three existing (commonly used) image/video training databases under the same training and evaluation configurations. The overall additional coding improvements by using the proposed database for all tested coding modules and CNN architectures are up to 10.3% based on the assessment of PSNR and 8.1% based on VMAF.