basicsr.metrics.__init__

basicsr.metrics.__init__.calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs)[source]

Calculate NIQE (Natural Image Quality Evaluator) metric.

Paper: Making a "Completely Blind" Image Quality Analyzer

This implementation could produce almost the same results as the official MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip

> MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296)

We use the official params estimated from the pristine dataset. We use the recommended block size (96, 96) without overlaps.

Parameters:

  • img (ndarray) – Input image whose quality needs to be computed. The input image must be in range [0, 255] with float/int type. The input_order of image can be ‘HW’ or ‘HWC’ or ‘CHW’. (BGR order) If the input order is ‘HWC’ or ‘CHW’, it will be converted to gray or Y (of YCbCr) image according to the convert_to argument.

  • crop_border (int) – Cropped pixels in each edge of an image. These pixels are not involved in the metric calculation.

  • input_order (str) – Whether the input order is ‘HW’, ‘HWC’ or ‘CHW’. Default: ‘HWC’.

  • convert_to (str) – Whether converted to ‘y’ (of MATLAB YCbCr) or ‘gray’. Default: ‘y’.

Returns:

NIQE result.

Return type:

float

basicsr.metrics.__init__.calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs)[source]

Calculate PSNR (Peak Signal-to-Noise Ratio).

Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

Parameters:

  • img (ndarray) – Images with range [0, 255].

  • img2 (ndarray) – Images with range [0, 255].

  • crop_border (int) – Cropped pixels in each edge of an image. These pixels are not involved in the calculation.

  • input_order (str) – Whether the input order is ‘HWC’ or ‘CHW’. Default: ‘HWC’.

  • test_y_channel (bool) – Test on Y channel of YCbCr. Default: False.

Returns:

PSNR result.

Return type:

float

basicsr.metrics.__init__.calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs)[source]

Calculate SSIM (structural similarity).

Paper: Image quality assessment: From error visibility to structural similarity

The results are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/.

For three-channel images, SSIM is calculated for each channel and then averaged.

Parameters:

  • img (ndarray) – Images with range [0, 255].

  • img2 (ndarray) – Images with range [0, 255].

  • crop_border (int) – Cropped pixels in each edge of an image. These pixels are not involved in the calculation.

  • input_order (str) – Whether the input order is ‘HWC’ or ‘CHW’. Default: ‘HWC’.

  • test_y_channel (bool) – Test on Y channel of YCbCr. Default: False.

Returns:

SSIM result.

Return type:

float