# ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression

arXiv: https://arxiv.org/abs/2410.09706 · HTML: https://mlsanity.com/paper/2410.09706/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2024.

## Built on by (most cited first, 20 of 20)

- [Neural Video Compression with Context Modulation](https://mlsanity.com/paper/2505.14541/) (2025)
- [Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance](https://mlsanity.com/paper/2512.07480/) (2025)
- [GIViC: Generative Implicit Video Compression](https://mlsanity.com/paper/2503.19604/) (2025)
- [T-GVC: Trajectory-Guided Generative Video Coding at Ultra-Low Bitrates](https://mlsanity.com/paper/2507.07633/) (2025)
- [BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression](https://mlsanity.com/paper/2505.09193/) (2025)
- [Neural B-frame Video Compression with Bi-directional Reference Harmonization](https://mlsanity.com/paper/2511.08938/) (2025)
- [Real-Time Neural Video Compression with Unified Intra and Inter Coding](https://mlsanity.com/paper/2510.14431/) (2025)
- [Generative Video Compression with One-Dimensional Latent Representation](https://mlsanity.com/paper/2603.15302/) (2026)
- [DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression](https://mlsanity.com/paper/2601.20564/) (2026)
- [Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales](https://mlsanity.com/paper/2606.28163/) (2026)
- [Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development](https://mlsanity.com/paper/2602.00483/) (2026)
- [Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression](https://mlsanity.com/paper/2603.05756/) (2026)
- [Efficient Video Diffusion with Sparse Information Transmission for Video Compression](https://mlsanity.com/paper/2603.18501/) (2026)
- [Neural Video Compression with Domain Transfer](https://mlsanity.com/paper/2605.13476/) (2026)
- [KD-NVC: A Search-and-Distill Framework to Accelerate Neural Video Coding](https://mlsanity.com/paper/2606.04595/) (2026)
- [High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning](https://mlsanity.com/paper/2606.13957/) (2026)
- [DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer](https://mlsanity.com/paper/2608.20515/) (2026)
- [Scalable Neural Video Representation Compression](https://mlsanity.com/paper/2609.04273/) (2026)
- [Video Compression with Graph-inspired Neural Representation](https://mlsanity.com/paper/2609.05541/) (2026)
- [Event-guided Neural Video Compression](https://mlsanity.com/paper/2610.02265/) (2026)

## Abstract (arXiv)

In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion performance. Existing LVCs mainly focus on mining the temporal movements while neglecting non-local correlations among frames. Additionally, current contextual video compression models use a single reference frame, which is insufficient for handling complex movements. To address these issues, we propose leveraging non-local correlations across multiple frames to enhance temporal priors, significantly boosting rate-distortion performance. To mitigate error accumulation, we introduce a partial cascaded fine-tuning strategy that supports fine-tuning on full-length sequences with constrained computational resources. This method reduces the train-test mismatch in sequence lengths and significantly decreases accumulated errors. Based on the proposed techniques, we present a video compression scheme ECVC. Experiments demonstrate that our ECVC achieves state-of-the-art performance, reducing 10.5% and 11.5% more bit-rates than previous SOTA method DCVC-FM over VTM-13.2 low delay B (LDB) under the intra period (IP) of 32 and -1, respectively.