# ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing

arXiv: https://arxiv.org/abs/2111.15536 · HTML: https://mlsanity.com/paper/2111.15536/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content](https://mlsanity.com/paper/2411.13362/) (2024)
- [Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation](https://mlsanity.com/paper/2312.02605/) (2023)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)
- [Compressed Video Super-Resolution based on Hierarchical Encoding](https://mlsanity.com/paper/2506.14381/) (2025)

## Abstract (arXiv)

This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the decoder to restore their original format and enhance reconstruction quality. ViSTRA3 has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under the Joint Video Exploration Team Common Test Conditions. Bjonegaard Delta (BD) measurement results show that the proposed framework consistently outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7% based on the assessment of PSNR and VMAF.