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NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network.

Shangdong Liu1, Puming Cao1, Yujian Feng1

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

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Summary
This summary is machine-generated.

This study introduces a novel neural representation approach for video compression (NRVC) that uses implicit neural representation (INR) for a more efficient model. NRVC enhances video compression performance and quality compared to existing methods.

Keywords:
attention mechanismimplicit neural representationvideo compression

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • End-to-end deep models for video compression are advancing but often complex and parameter-heavy.
  • Implicit Neural Representation (INR) offers a lightweight alternative but faces limitations in feature extraction fidelity.
  • Existing INR methods struggle to accurately fit the complex mapping functions required for video frames.

Purpose of the Study:

  • To develop a more effective and lightweight video compression method using implicit neural representation.
  • To address the limitations of feature extraction singularity in current INR-based video compression techniques.
  • To improve the fitting accuracy of the mapping function for video frames in neural network-based compression.

Main Methods:

  • Proposed a Neural Representation approach for Video Compression (NRVC) utilizing an implicit multiscale fusion network.
  • Incorporated normalized residual networks to enhance the effectiveness of INR in fitting target functions.
  • Introduced the Multiscale Representations for Video Compression (MSRVC) network for robust feature extraction.
  • Developed a Feature Extraction Channel Attention (FECA) block to capture inter-channel feature interactions.

Main Results:

  • NRVC demonstrated a 2.16% increase in decoded Peak Signal-to-Noise Ratio (PSNR) compared to the NeRV method at similar Bits Per Pixel (BPP).
  • The proposed NRVC method significantly outperformed the conventional High Efficiency Video Coding (HEVC) standard in terms of PSNR.
  • The multiscale fusion and channel attention mechanisms effectively improved the network's ability to fit the video frame mapping function.

Conclusions:

  • NRVC offers a superior alternative to existing INR-based video compression methods, balancing model efficiency with high performance.
  • The integration of multiscale features and channel attention significantly enhances the capability of neural representations for video compression.
  • This approach provides a promising direction for developing next-generation, efficient, and high-quality video compression technologies.