ssim | 易学教程

Working with SSIM loss function in tensorflow for RGB images

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问题 I want to use SSIM metric as my loss function for the model I'm working on in tensorflow . SSIM should measure the similarity between my reconstructed output image of my denoising autoencoder and the input uncorrupted image (RGB) . As of what I understood, for using the SSIM metric in tensorflow, the images should be normalized to [0,1] or [0,255] and not [-1,1]. After converting my tensors to [0,1] and implementing SSIM as my loss function, the reconstructed image is black and white instead

计算两幅图像的PSNR和SSIM的python源码

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import math import numpy as np from skimage import io from scipy . signal import convolve2d def compute_psnr ( img1 , img2 ) : if isinstance ( img1 , str ) : img1 = io . imread ( img1 ) if isinstance ( img2 , str ) : img2 = io . imread ( img2 ) mse = np . mean ( ( img1 / 255 . - img2 / 255 . ) ** 2 ) if mse < 1.0e-10 : return 1000000000000 PIXEL_MAX = 1 psnr = 20 * math . log10 ( PIXEL_MAX / math . sqrt ( mse ) ) return mse , psnr def matlab_style_gauss2D ( shape = ( 3 , 3 ) , sigma = 0.5 ) : """ 2D gaussian mask - should give the same result as MATLAB's fspecial('gaussian',[shape],[sigma]) ""

Psychovisual image similarity algorithm/library

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问题 I'm looking for an algorithm (ideally a C/C++ implementation) that calculates perceived similarity between two images, taking into account psychovisual factors (e.g. that difference in chroma is not as bad as difference in brightness). I have original image and multiple variations of it (256-color quantisations in my case) and I'd like algorithm to find which image a human would judge as the best one. The best I've found so far is SSIM, but it doesn't "understand" dithering (error diffusion)

How to find PSNR and SSIM of two video files in python using openCV and other libraries?

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问题 I want to find out PSNR and SSIM of two video files in python using openCv and numpy. How to find PSNR in python I tried below code for SSIM # compute the Structural Similarity Index (SSIM) between the two # images, ensuring that the difference image is returned (score, diff) = compare_ssim(grayA, grayB, full=True) diff = (diff * 255).astype("uint8") print("SSIM: {}".format(score)) # threshold the difference image, followed by finding contours to # obtain the regions of the two input images

SSIM / MS-SSIM for TensorFlow

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问题 Is there a SSIM or even MS-SSIM implementation for TensorFlow ? SSIM ( structural similarity index metric ) is a metric to measure image quality or similarity of images. It is inspired by human perception and according to a couple of papers, it is a much better loss-function compared to l1/l2. For example, see Loss Functions for Neural Networks for Image Processing. Up to now, I could not find an implementation in TensorFlow. And after trying to do it by myself by porting it from C++ or

mse+psnr+ssim 批量

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from skimage.measure import compare_ssim, compare_mse, compare_psnr import cv2 import os import numpy as np cnt=0 HD_path=r"C:\Users\Song\Desktop\test\HD" LC_path=r"C:\Users\Song\Desktop\test\LC" HD_files = os.listdir(HD_path) for HD_file in HD_files: cnt+=1 print(cnt) i=1 mse =np.empty(cnt) psnr=np.empty(cnt) ssim=np.empty(cnt) while(i<=cnt): img1=cv2.imread(os.path.join(HD_path,"%d.png"%i))#图片从1.png开始 img2=cv2.imread(os.path.join(LC_path,"%d.png"%i)) j=i-1#数组下标从0开始 mse[j] = compare_mse(img1,img2) ssim[j] = compare_ssim(img1, img2, data_range=255,multichannel=True) psnr[j] = compare_psnr(img1

How to assign values to specified location in Tensorflow?

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问题 I would like to implement a SSIM loss function, since the boarders are aborted by the convolution, I would like to preserve the boarders and compute L1 loss for the pixels of boarder. The code are learned from here. SSIM / MS-SSIM for TensorFlow For example, we hava img1 and img2 size [batch,32,32,32,1], and the window_size of Guassian 11, the result ssim map will be [batch,22,22,22,1], L1 map [batch,32,32,32,1] how can I assign ssim to the center of the L1? I receive error like this；

What is the accurate way of calculating SSIM score (with ffmpeg) between a video stream and the reference video?

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问题 As ffmpeg calculates ssim score by a frame-by-frame comparision, how do I exactly align the reference video (say ref.mp4) with the video stream, so that only the corresponding frames (ith frame of both the videos, for all i) are compared by ffmpeg ? Please help. 来源： https://stackoverflow.com/questions/36156140/what-is-the-accurate-way-of-calculating-ssim-score-with-ffmpeg-between-a-video

PSNR和SSIM

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PSNR（Peak Signal to Noise Ratio）峰值信噪比，一种全参考的图像质量评价指标。其中，MSE表示当前图像X和参考图像Y的均方误差（Mean Square Error），H、W分别为图像的高度和宽度；n为每像素的比特数，一般取8，即像素灰阶数为256. PSNR的单位是dB，数值越大表示失真越小。 PSNR是最普遍和使用最为广泛的一种图像客观评价指标，然而它是基于对应像素点间的误差，即基于误差敏感的图像质量评价。由于并未考虑到人眼的视觉特性（人眼对空间频率较低的对比差异敏感度较高，人眼对亮度对比差异的敏感度较色度高，人眼对一个区域的感知结果会受到其周围邻近区域的影响等），因而经常出现评价结果与人的主观感觉不一致的情况。 SSIM（structural similarity）结构相似性，也是一种全参考的图像质量评价指标，它分别从亮度、对比度、结构三方面度量图像相似性。其中u x 、u y 分别表示图像X和Y的均值，σ X 、σ Y 分别表示图像X和Y的方差，σ XY 表示图像X和Y的协方差，即 C1、C2、C3为常数，为了避免分母为0的情况，通常取C1=(K1*L)^2, C2=(K2*L)^2, C3=C2/2, 一般地K1=0.01, K2=0.03, L=255. 则 SSIM取值范围[0,1]，值越大，表示图像失真越小. 在实际应用中