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(Informative)

1. Introduction

This chapter provides the Neural Network weights obtained by applying the process specified in:

  1.  Design Procedure to two important cases.
  2.  Complexity Reduction to the Neural Network of point 1.

2. Weights for important applications

The weights of the SD (540×960) to HD (1080×1920) and HD (1080×1920) to UHD (2160×3840) up-sampling filters will be downloadable from the MPAI Git.

The performance of the un-pruned and pruned network has been verified according to Table 1:

Table 1 –

Standard sequences  CatRobot, FoodMarket4, ParkRunning3.
Bits/sample  8 and 10 bit-depth per component.
Colour space  YCbCr with 4:2:0 sub sampling.
Encoding technologies  AVC, HEVC, and VVC.
Encoding settings  Random Access and Low Delay at QPs 22, 27, 32, 37, 42, 47.
Up-sampling SD to HD and HD to UHD.
Metrics BD-Rate, BD-PSNR and BD-VMAF
Deep-learning structure Same for all QPs

3. Weights of the complexity-reduced network

The number of parameters of the pruned network is about  of the original

The loss in performance of the pruned network is less than 1% in BD-rate compared to the network out of the Design Procedure.

The performance of the un-pruned and pruned network has been verified using the same parameters of Table 1

Results show an impressive improvement for all coding technologies, and encoding options for all three objective metrics when compared with the currently used traditional bicubic interpolation. In Table 2, LD stands for low delay and RA for Random Access

Table 2 – Performance of the EVC-UFV Up-sampling Filter

HEVC (LD) VVC (LD) HEVC (RA) VVC (RA)
SD to HD (using own trained filter) 12.2% 13.8% 17.3% 22.5%
HD to UHD (using own trained filter) 6% 6.5% 6.0% 7.9%
SD to HD (using HD to UHD filter) 11.6% 11.4% 15.3% 19.9%

The parameters of the u-pruned and pruned networks will be available at the MPAI Git.

 

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