# Multitask Learning for VVC Quality Enhancement and Super-Resolution

arXiv: https://arxiv.org/abs/2104.08319 · HTML: https://mlsanity.com/paper/2104.08319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Loss Functions for Neural Networks for Image Processing](https://mlsanity.com/paper/1511.08861/) (2015)
- [Gated Fusion Network for Joint Image Deblurring and Super-Resolution](https://mlsanity.com/paper/1807.10806/) (2018)
- [Deep Convolution Networks for Compression Artifacts Reduction](https://mlsanity.com/paper/1608.02778/) (2016)
- [Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network](https://mlsanity.com/paper/1805.03894/) (2018)
- [Versatile video coding and super-resolution for efficient delivery of 8K video with 4K backward-compatibility](https://mlsanity.com/paper/2002.06922/) (2020)

## Built on by (most cited first, 1 of 1)

- [CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability](https://mlsanity.com/paper/2202.00416/) (2022)

## Abstract (arXiv)

The latest video coding standard, called versatile video coding (VVC), includes several novel and refined coding tools at different levels of the coding chain. These tools bring significant coding gains with respect to the previous standard, high efficiency video coding (HEVC). However, the encoder may still introduce visible coding artifacts, mainly caused by coding decisions applied to adjust the bitrate to the available bandwidth. Hence, pre and post-processing techniques are generally added to the coding pipeline to improve the quality of the decoded video. These methods have recently shown outstanding results compared to traditional approaches, thanks to the recent advances in deep learning. Generally, multiple neural networks are trained independently to perform different tasks, thus omitting to benefit from the redundancy that exists between the models. In this paper, we investigate a learning-based solution as a post-processing step to enhance the decoded VVC video quality. Our method relies on multitask learning to perform both quality enhancement and super-resolution using a single shared network optimized for multiple degradation levels. The proposed solution enables a good performance in both mitigating coding artifacts and super-resolution with fewer network parameters compared to traditional specialized architectures.