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

arXiv 2607.16319 ↗First posted 15 Jul 2026cs.CVcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 127 references that are in the map. First posted on arXiv 15 Jul 2026.

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26 of 127 references.

References · 26

  1. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  2. 2015Compression Artifacts Reduction by a Deep Convolutional Networkcited by 136
  3. 2018Multi-Frame Quality Enhancement for Compressed Videocited by 48
  4. 2017Enhancing Quality for HEVC Compressed Videoscited by 28
  5. 2020Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Imagescited by 28
  6. 2016A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Codingcited by 26
  7. 2020Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Videocited by 26
  8. 2024CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancementcited by 17
  9. 2020MFRNet: A New CNN Architecture for Post-Processing and In-loop Filteringcited by 15
  10. 2020Efficient Adaptation of Neural Network Filter for Video Compressioncited by 13
  11. 2019Quality-Gated Convolutional LSTM for Enhancing Compressed Videocited by 11
  12. 2019A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVCcited by 10
14 more
  1. 2020Multi-scale Grouped Dense Network for VVC Intra Codingcited by 10
  2. 2018Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Networkcited by 8
  3. 2017Spatial-Temporal Residue Network Based In-Loop Filter for Video Codingcited by 5
  4. 2020Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Codingcited by 5
  5. 2025Plug-and-Play Versatile Compressed Video Enhancementcited by 5
  6. 2018MGANet: A Robust Model for Quality Enhancement of Compressed Videocited by 4
  7. 2019Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structurecited by 4
  8. 2020An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Codingcited by 4
  9. 2018A practical convolutional neural network as loop filter for intra framecited by 3
  10. 2021A CNN-based Prediction-Aware Quality Enhancement Framework for VVCcited by 3
  11. 2025Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Modelcited by 3
  12. 2021Model Selection CNN-based VVC QualityEnhancementcited by 2
  13. 2021Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Codingcited by 2
  14. 2021B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reductioncited by 1

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