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Cross-Modal Multivariate Pattern Analysis
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MPCNet: Compressed multi-view video restoration via motion-parallax complementation network.

Chang Wu1, Gang He2, Xinquan Lai1

  • 1School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 15, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Motion-Parallax Complementation Network (MPCNet) to enhance compressed multi-view video (MVV) restoration. MPCNet effectively utilizes temporal and parallax information, significantly improving video quality and reducing compression artifacts.

Keywords:
Deep neural networkMulti-view video codingStereo informationVideo compression restoration

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Area of Science:

  • Computer Vision
  • Video Processing
  • Machine Learning

Background:

  • Existing learning-based methods for compressed multi-view video (MVV) restoration have limited performance.
  • These methods often fail to leverage comprehensive information from temporal and parallax domains.
  • Compression artifacts in MVV stem from complex inter-frame, intra-frame, and inter-view reference errors.

Purpose of the Study:

  • To propose an efficient network for restoring the quality of compressed MVV.
  • To effectively utilize stereo information from both temporal and parallax domains.
  • To improve the representational ability and restoration performance for compressed MVV.

Main Methods:

  • Introduction of a Motion-Parallax Complementation Network (MPCNet) with coarse and fine stages.
  • Mutual compensation of features from multiple domains for step-by-step information aggregation.
  • Development of an attention-based feature filtering and modulation module (AFFM) for efficient feature fusion and suppression of misleading information.

Main Results:

  • MPCNet achieved an average PSNR increase of 1.978 dB and MS-SSIM increase of 0.0282.
  • Significant BD-rate reduction averaging 47.342% was observed.
  • Improvements in high-level vision tasks, including mIoU for semantic segmentation (0.352) and mAP for object detection (51.71).

Conclusions:

  • MPCNet demonstrates superior performance in restoring compressed MVV compared to state-of-the-art methods.
  • The proposed network effectively eliminates compression distortions and enhances subjective video quality.
  • The approach offers benefits for subsequent high-level computer vision tasks.