metrics.py 文件源码

def _compute_msssim(imQual, nlevels=5, sigma=1.2, L=1, K=(0.01, 0.03)):
    '''
    An implementation of the Multi-Scale Structural SIMilarity index (MS-SSIM).

    References
    -------------
    Multi-scale Structural Similarity Index (MS-SSIM)
    Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural
    similarity for image quality assessment," Invited Paper, IEEE Asilomar
    Conference on Signals, Systems and Computers, Nov. 2003

    Parameters
    -------------
    imQual : ImageQuality
    nlevels : int
        The max number of levels to analyze
    sigma : float
        Sets the standard deviation of the gaussian filter. This setting
        determines the minimum scale at which quality is assessed.
    L : scalar
        The dynamic range of the data. This value is 1 for float
        representations and 2^bitdepth for integer representations.
    K : 2-tuple
        A list of two constants which help prevent division by zero.

    Returns
    -------
    imQual : ImageQuality
        A struct used to organize image quality information. NOTE: the valid
        range for SSIM is [-1, 1].
    '''
    _full_reference_input_check(imQual, sigma, nlevels, L)

    img1 = imQual.orig
    img2 = imQual.recon

    # The relative imporance of each level as determined by human experiment
    # weight = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]

    for level in range(0, nlevels):
        imQual += _compute_ssim(ImageQuality(img1, img2), sigma=sigma, L=L,
                                K=K, scale=sigma * 2**level)
        if level == nlevels - 1:
            break

        # Downsample (using ndimage.zoom to prevent sampling bias)
        img1 = scipy.ndimage.zoom(img1, 1/2)
        img2 = scipy.ndimage.zoom(img2, 1/2)

    return imQual