# ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation

arXiv: https://arxiv.org/abs/1911.02833 · HTML: https://mlsanity.com/paper/1911.02833/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming](https://mlsanity.com/paper/2211.08428/) (2022)
- [Immersive Video Compression using Implicit Neural Representations](https://mlsanity.com/paper/2402.01596/) (2024)
- [Compressing Deep Image Super-resolution Models](https://mlsanity.com/paper/2401.00523/) (2023)
- [C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales](https://mlsanity.com/paper/2503.13740/) (2025)
- [Estimating the Resize Parameter in End-to-end Learned Image Compression](https://mlsanity.com/paper/2204.12022/) (2022)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)
- [CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability](https://mlsanity.com/paper/2202.00416/) (2022)

## Abstract (arXiv)

We present a new video compression framework (ViSTRA2) which exploits adaptation of spatial resolution and effective bit depth, down-sampling these parameters at the encoder based on perceptual criteria, and up-sampling at the decoder using a deep convolution neural network. ViSTRA2 has been integrated with the reference software of both the HEVC (HM 16.20) and VVC (VTM 4.01), and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. Our results show consistent and significant compression gains against HM and VVC based on Bjonegaard Delta measurements, with average BD-rate savings of 12.6% (PSNR) and 19.5% (VMAF) over HM and 5.5% (PSNR) and 8.6% (VMAF) over VTM.