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Parameter Adaptive Network for Large-Scale Neural In-Loop Filtering in Versatile Video Coding.

Yuansheng Wu1, Fan Cai2, Xiaodan Song2

  • 1National Key Laboratory of Complex Aviation System Simulation, Southwest China Institute of Electronic Technology, Chengdu 610036, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces an efficient parameter-adaptive in-loop filtering network for Versatile Video Coding (H.266/VVC). The novel approach achieves significant bitrate savings without increasing overhead or computational cost.

Area of Science:

  • Video Coding Technologies
  • Digital Signal Processing
  • Machine Learning for Video Compression

Background:

  • Efficient in-loop filtering is crucial for Versatile Video Coding (H.266/VVC) performance.
  • Existing parameter-adaptive methods incur high bitstream overhead or excessive model parameters.
  • Current parameter-generation networks struggle with efficiency in large-scale video compression models.

Purpose of the Study:

  • To develop an efficient parameter-adaptive in-loop filtering network for H.266/VVC.
  • To address the limitations of existing methods regarding overhead and parameter count.
  • To enhance reconstruction performance and parameter efficiency in video coding.

Main Methods:

  • Modeling convolutional parameters as a linear combination of pre-trained kernels with adaptive weights via input-driven attention.
Keywords:
adaptive parameterconvolution neural networkgradientin-loop filteringversatile video coding

Related Experiment Videos

  • Proposing a multi-scale parameter-adaptive convolution and its extension with side information.
  • Incorporating gradient information to improve distortion guidance from side information.

    • Main Results:

    • Achieved bitrate savings of 7.89% for Y, 18.25% for U, and 19.15% for V components.
    • Outperformed fixed-parameter baselines by an average of 1.41% in bitrate savings.
    • Demonstrated negligible computational overhead compared to existing methods.

    Conclusions:

    • The proposed efficient parameter-adaptive in-loop filtering network effectively reduces bitrate without significant computational cost.
    • The novel approach balances parameter efficiency and reconstruction performance for H.266/VVC.
    • This method offers a practical solution for advanced video compression standards.