The project covers common metrics for super-resolution performance evaluation.

Related tags

Deep LearningSR-Metrics
Overview

Super-Resolution Performance Evaluation Code

The project covers common metrics for super-resolution performance evaluation.

Metrics support

The scripts will calculate the values of the following evaluation metrics: 'MA', 'NIQE', 'PI', 'PSNR', 'BRISQUE', 'SSIM', 'MSE', 'RMSE', 'MAE', 'LPIPS'. Note that the 'SSIM' values are calculated by ssim.m, the matlab code including the suggested downsampling process available in this link.

Highlights

  • Breakpoint continuation support : The program can continue from where it was last interrupted by using .xlsx file
  • Parallel computing support : The Programs can be re-scaled to take advantage of multi-core performance by using pythonThreadPoolExecutor
  • Both RGB and YCbCr color space support

Dependencies

Instructions for use this code

Please ref BLIND IMAGE QUALITY TOOLBOX

Reference

The code is based on SPSR and BIQT.

Owner
xmy
xmy
GitHub Repository
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