# CANF-VC++: Enhancing Conditional Augmented Normalizing Flows for Video Compression with Advanced Techniques

arXiv: https://arxiv.org/abs/2309.05382 · HTML: https://mlsanity.com/paper/2309.05382/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration](https://mlsanity.com/paper/2408.05042/) (2024)
- [Learned Rate Control for Frame-Level Adaptive Neural Video Compression via Dynamic Neural Network](https://mlsanity.com/paper/2508.20709/) (2025)

## Abstract (arXiv)

Video has become the predominant medium for information dissemination, driving the need for efficient video codecs. Recent advancements in learned video compression have shown promising results, surpassing traditional codecs in terms of coding efficiency. However, challenges remain in integrating fragmented techniques and incorporating new tools into existing codecs. In this paper, we comprehensively review the state-of-the-art CANF-VC codec and propose CANF-VC++, an enhanced version that addresses these challenges. We systematically explore architecture design, reference frame type, training procedure, and entropy coding efficiency, leading to substantial coding improvements. CANF-VC++ achieves significant Bjontegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H.266 reference software VTM. Our work demonstrates the potential of integrating advancements in video compression and serves as inspiration for future research in the field.