# Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure

arXiv: https://arxiv.org/abs/1901.09575 · HTML: https://mlsanity.com/paper/1901.09575/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [Prediction-Aware Quality Enhancement of VVC Using CNN](https://mlsanity.com/paper/2112.04225/) (2021)
- [Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference](https://mlsanity.com/paper/2511.16137/) (2025)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)

## Abstract (arXiv)

In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS). The proposed network consists of a temporal structure fusion subnet and a spatial detail enhancement subnet. The former subnet is used to estimate and compensate the temporal motion across frames, and the latter subnet is used to reduce the compression artifacts and enhance the reconstruction quality of compressed video. Experimental results demonstrate the effectiveness of our SDTS-based method.