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', 'PSNR-Y', 'SSIM', 'SSIM-Y' 'BRISQUE', 'LPIPS'.

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
• One2Many support : Since there are many SR methods that allow multiple predictions for a given low resolution image. In order to simplify the configuration, it is specially designed.

Dependencies

• Python 3
• PyTorch >= 1.0
• Matlab (IMAGE TOOLBOX required)

Instructions for use this code

Please ref BLIND IMAGE QUALITY TOOLBOX

Note

• Please ensure that SR and GT are the same size.

Reference

The code is based on SPSR and BIQT.