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Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images.

Huan Zhang1, Jiangzhong Cao1, Dongsheng Zheng1

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.

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Summary
This summary is machine-generated.

Deep learning models for synthesized view quality enhancement (SVQE) struggle with limited data. This study introduces a new method using synthetic data and a distortion mask to significantly improve model performance in multi-view video systems.

Keywords:
data augmentationquality enhancementsynthesized viewsynthetic images

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Deep learning models aim to enhance synthesized view quality in multi-view video systems, but are limited by small training datasets due to a lack of Multi-view Video plus Depth (MVD) data.
  • Existing models face challenges in addressing distortions from compression and Depth Image-Based Rendering (DIBR).

Purpose of the Study:

  • To address the data scarcity issue for Synthesized View Quality Enhancement (SVQE) models.
  • To improve the performance of deep learning-based SVQE models by augmenting training data with synthetic synthesized view images (SVIs) and incorporating a DIBR distortion mask prediction network.

Main Methods:

  • A novel random irregular polygon-based method synthesizes SVIs from existing RGB/RGBD data to simulate DIBR distortion.
  • A synthetic synthesized view database is constructed, including SVIs and DIBR distortion masks.
  • A DIBR distortion mask prediction network is embedded into SVQE models to precisely locate and characterize DIBR distortions.

Main Results:

  • Pre-training existing SVQE models (DnCNN, NAFNet, TSAN) on the synthetic dataset significantly improved PSNR and MPPSNRr performance.
  • Average PSNR gains of 0.51 dB (DnCNN), 0.36 dB (NAFNet), and 0.26 dB (TSAN) were observed.
  • Average MPPSNRr gains of 0.86 (DnCNN), 0.25 (NAFNet), and 0.24 (TSAN) were achieved.
  • Embedding the DIBR distortion mask prediction network further enhanced PSNR by 0.02-0.03 dB and MPPSNRr by 0.004-0.121 dB for DnCNN and NAFNet.

Conclusions:

  • Data augmentation using synthetic SVIs and incorporating a DIBR distortion mask prediction network is an effective strategy to boost SVQE model performance.
  • The proposed methods demonstrate significant improvements in image quality metrics for multi-view video systems.
  • This approach offers a feasible solution for training more robust and accurate SVQE models despite data limitations.