cilissa.metrics
- Image quality metrics
Classes
- class cilissa.metrics.MSE(rmse: bool = False)[source]
Mean squared error (MSE)
Average squared difference between the estimated values and the actual value.
References
- class cilissa.metrics.PSNR(max_pixel_value: Optional[int] = None)[source]
Peak signal-to-noise ratio (PSNR)
Ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation.
References
- class cilissa.metrics.SSIM(channels_num: Optional[int] = None, sigma: float = 1.5, truncate: float = 3.5, K1: float = 0.01, K2: float = 0.03)[source]
Structural similarity index measure (SSIM)
The SSIM Index quality assessment index is based on the computation of three terms, namely the luminance term, the contrast term and the structural term. The overall index is a multiplicative combination of the three terms.
- Parameters
channels_num (int/None) – If None, image is assumed to be grayscale (single channel). Otherwise the number of channels should be specified here.
- Returns
Overall quality measure of the entire image (MSSIM)
- Return type
float
References
- class cilissa.metrics.UIQI(channels_num: Optional[int] = None, block_size: int = 8)[source]
Universal Image Quality Index (UIQI)
Combines loss of correlation, luminance distortion and contrast distortion. Predecessor of SSIM metric.
References
- class cilissa.metrics.VIFP(channels_num: Optional[int] = None, sigma: float = 2.0)[source]
Pixel Based Visual Information Fidelity (VIF-P)
Employs natural scene statistical (NSS) models in conjunction with a distortion (channel) model to quantify the information shared between the test and the reference images.
The pixel domain algorithm is not described in the paper below. This is a computationally simpler derivative of the algorithm presented in the paper, based on the original Matlab implementation.
References