# Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis

arXiv: https://arxiv.org/abs/2607.16319 · HTML: https://mlsanity.com/paper/2607.16319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 127 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 26 of 26)

- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [Multi-Frame Quality Enhancement for Compressed Video](https://mlsanity.com/paper/1803.04680/) (2018)
- [Enhancing Quality for HEVC Compressed Videos](https://mlsanity.com/paper/1709.06734/) (2017)
- [Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images](https://mlsanity.com/paper/2006.16581/) (2020)
- [A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding](https://mlsanity.com/paper/1608.06690/) (2016)
- [Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2008.00499/) (2020)
- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering](https://mlsanity.com/paper/2007.07099/) (2020)
- [Efficient Adaptation of Neural Network Filter for Video Compression](https://mlsanity.com/paper/2007.14267/) (2020)
- [A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC](https://mlsanity.com/paper/1903.01648/) (2019)
- [Quality-Gated Convolutional LSTM for Enhancing Compressed Video](https://mlsanity.com/paper/1903.04596/) (2019)
- [Multi-scale Grouped Dense Network for VVC Intra Coding](https://mlsanity.com/paper/2005.07896/) (2020)
- [Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network](https://mlsanity.com/paper/1805.03894/) (2018)
- [Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding](https://mlsanity.com/paper/1709.08462/) (2017)
- [Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure](https://mlsanity.com/paper/1901.09575/) (2019)
- [Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding](https://mlsanity.com/paper/2006.06392/) (2020)
- [Plug-and-Play Versatile Compressed Video Enhancement](https://mlsanity.com/paper/2504.15380/) (2025)
- [MGANet: A Robust Model for Quality Enhancement of Compressed Video](https://mlsanity.com/paper/1811.09150/) (2018)
- [An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding](https://mlsanity.com/paper/2012.15003/) (2020)
- [A practical convolutional neural network as loop filter for intra frame](https://mlsanity.com/paper/1805.06121/) (2018)
- [A CNN-based Prediction-Aware Quality Enhancement Framework for VVC](https://mlsanity.com/paper/2105.05658/) (2021)
- [Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model](https://mlsanity.com/paper/2504.19935/) (2025)
- [Model Selection CNN-based VVC QualityEnhancement](https://mlsanity.com/paper/2105.03338/) (2021)
- [Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding](https://mlsanity.com/paper/2106.08936/) (2021)
- [B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction](https://mlsanity.com/paper/2101.09021/) (2021)

## Abstract (arXiv)

As Ultra-High-Definition (UHD) displays and immersive media services become ubiquitous in the Internet of Things (IoT) and Consumer Electronics (CE) sectors, including 8K display and mobile devices, the demand for high-efficiency video coding is unprecedented. While Deep Learning-based Filtering (DLF) has emerged as a promising solution to mitigate compression artifacts inherent in standards like High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266), its deployment in CE devices is severely constrained by computational complexity, memory bandwidth, and power consumption. To bridge the gap between academic research and practical deployment, this paper presents a comprehensive, hardware-oriented survey of DLF techniques. We propose a systematic three-dimensional taxonomy classifying methods into (1) Integration Scheme within the Video Coding, (2) Coding Information Utilization, and (3) Network Design Strategy. Unlike prior reviews, this work critically analyzes the trade-offs between Rate-Distortion (RD) performance and hardware feasibility, highlighting the evolution from heavy, performance-oriented models to lightweight, hardware-friendly architectures targeting Neural Processing Units (NPUs). Furthermore, we incorporate the latest standardization activities from the Joint Video Experts Team (JVET) on Neural Network-based Video Coding (NNVC) to provide realistic guidelines. We also identify open challenges such as real-time inference latency and error propagation, providing a roadmap toward robust, low-power intelligent video coding in next-generation CE vision endpoints.