# OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement

arXiv: https://arxiv.org/abs/2208.04661 · HTML: https://mlsanity.com/paper/2208.04661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding](https://mlsanity.com/paper/1608.06690/) (2016)
- [DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal](https://mlsanity.com/paper/1806.03275/) (2018)
- [Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC](https://mlsanity.com/paper/1912.11604/) (2019)

## Abstract (arXiv)

Convolution neural network (CNN) based methods offer effective solutions for enhancing the quality of compressed image and video. However, these methods ignore using the raw data to enhance the quality. In this paper, we adopt the raw data in the quality enhancement for the HEVC intra-coded image by proposing an online learning-based method. When quality enhancement is demanded, we online train our proposed model at encoder side and then use the parameters to update the model of decoder side. This method not only improves model performance, but also makes one model adoptable to multiple coding scenarios. Besides, quantization error in discrete cosine transform (DCT) coefficients is the root cause of various HEVC compression artifacts. Thus, we combine frequency domain priors to assist image reconstruction. We design a DCT based convolution layer, to produce DCT coefficients that are suitable for CNN learning. Experimental results show that our proposed online learning based dual-domain network (OL-DN) has achieved superior performance, compared with the state-of-the-art methods.