# A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

arXiv: https://arxiv.org/abs/1608.06690 · HTML: https://mlsanity.com/paper/1608.06690/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2016.

## Built on by (most cited first, 27 of 27)

- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [In-Loop Filtering via Trained Look-Up Tables](https://mlsanity.com/paper/2407.10926/) (2024)
- [End-to-end Transformer for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2210.13827/) (2022)
- [Transcoded Video Restoration by Temporal Spatial Auxiliary Network](https://mlsanity.com/paper/2112.07948/) (2021)
- [Plug-and-Play Versatile Compressed Video Enhancement](https://mlsanity.com/paper/2504.15380/) (2025)
- [Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information](https://mlsanity.com/paper/2105.14962/) (2021)
- [End-to-End Learned Image Compression with Quantized Weights and Activations](https://mlsanity.com/paper/2111.09348/) (2021)
- [Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding](https://mlsanity.com/paper/2106.08936/) (2021)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [Online Video Quality Enhancement with Spatial-Temporal Look-up Tables](https://mlsanity.com/paper/2311.13616/) (2023)
- [B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction](https://mlsanity.com/paper/2101.09021/) (2021)
- [PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network](https://mlsanity.com/paper/2206.07893/) (2022)
- [In-Loop Filtering Using Learned Look-Up Tables for Video Coding](https://mlsanity.com/paper/2509.09494/) (2025)
- [Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression](https://mlsanity.com/paper/2102.06531/) (2021)
- [Multi-Density Attention Network for Loop Filtering in Video Compression](https://mlsanity.com/paper/2104.12865/) (2021)
- [Neural Network based Inter bi-prediction Blending](https://mlsanity.com/paper/2202.03149/) (2022)
- [Dilated convolutional neural network-based deep reference picture generation for video compression](https://mlsanity.com/paper/2202.05514/) (2022)
- [Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection](https://mlsanity.com/paper/2202.09802/) (2022)
- [Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding](https://mlsanity.com/paper/2206.05432/) (2022)
- [OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement](https://mlsanity.com/paper/2208.04661/) (2022)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)
- [Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression](https://mlsanity.com/paper/2311.04430/) (2023)
- [A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video](https://mlsanity.com/paper/2311.08746/) (2023)
- [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)
- [Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement](https://mlsanity.com/paper/2609.39894/) (2026)

## Abstract (arXiv)

Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup.