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来自：shiter编写程序的艺术
对计算图像相似度的方法，本文做了如下总结，主要有三种办法：
1.PSNR峰值信噪比PSNR（Peak Signal to Noise Ratio），一种全参考的图像质量评价指标。
简介：https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
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。
SSIM取值范围[0,1]，值越大，表示图像失真越小.
在实际应用中，可以利用滑动窗将图像分块，令分块总数为N，考虑到窗口形状对分块的影响，采用高斯加权计算每一窗口的均值、方差以及协方差，然后计算对应块的结构相似度SSIM，最后将平均值作为两图像的结构相似性度量，即平均结构相似性MSSIM：
参考资料[1] 峰值信噪比-维基百科
[2] 王宇庆，刘维亚，王勇. 一种基于局部方差和结构相似度的图像质量评价方法[J]. 光电子激光，2008。
[3]http://www.cnblogs.com/vincent2012/archive/2012/10/13/2723152.html
官方文档的说明，不过是GPU版本的，我们可以修改不用gpu不然还得重新编译
http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/highgui/video-input-psnr-ssim/video-input-psnr-ssim.html#videoinputpsnrmssim
http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/gpu/gpu-basics-similarity/gpu-basics-similarity.html?highlight=psnr
// PSNR.cpp : 定义控制台应用程序的入口点。//#include "stdafx.h"#include <iostream> // Console I/O#include <sstream> // String to number conversion#include <opencv2/core/core.hpp> // Basic OpenCV structures#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU#include <opencv2/highgui/highgui.hpp>// Read images#include <opencv2/gpu/gpu.hpp> // GPU structures and methodsusing namespace std;using namespace cv;double getPSNR(const Mat& I1, const Mat& I2); // CPU versionsScalar getMSSIM( const Mat& I1, const Mat& I2);double getPSNR_GPU(const Mat& I1, const Mat& I2); // Basic GPU versionsScalar getMSSIM_GPU( const Mat& I1, const Mat& I2);struct BufferPSNR // Optimized GPU versions{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later. gpu::GpuMat gI1, gI2, gs, t1,t2; gpu::GpuMat buf;};double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);struct BufferMSSIM // Optimized GPU versions{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later. gpu::GpuMat gI1, gI2, gs, t1,t2; gpu::GpuMat I1_2, I2_2, I1_I2; vector<gpu::GpuMat> vI1, vI2; gpu::GpuMat mu1, mu2; gpu::GpuMat mu1_2, mu2_2, mu1_mu2; gpu::GpuMat sigma1_2, sigma2_2, sigma12; gpu::GpuMat t3; gpu::GpuMat ssim_map; gpu::GpuMat buf;};Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);void help(){ cout << "\n--------------------------------------------------------------------------" << endl << "This program shows how to port your CPU code to GPU or write that from scratch." << endl << "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl << "Usage:" << endl << "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl << "--------------------------------------------------------------------------" << endl << endl;}int main(int argc, char *argv[]){ help(); Mat I1 = imread("swan1.jpg",1); // Read the two images Mat I2 = imread("swan2.jpg",1); if (!I1.data || !I2.data) // Check for success { cout << "Couldn't read the image"; return 0; } BufferPSNR bufferPSNR; BufferMSSIM bufferMSSIM; int TIMES; stringstream sstr("500"); sstr >> TIMES; double time, result; //------------------------------- PSNR CPU ---------------------------------------------------- time = (double)getTickCount(); for (int i = 0; i < TIMES; ++i) result = getPSNR(I1,I2); time = 1000*((double)getTickCount() - time)/getTickFrequency(); time /= TIMES; cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds." << " With result of: " << result << endl; ////------------------------------- PSNR GPU ---------------------------------------------------- //time = (double)getTickCount(); //for (int i = 0; i < TIMES; ++i) // result = getPSNR_GPU(I1,I2); //time = 1000*((double)getTickCount() - time)/getTickFrequency(); //time /= TIMES; //cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds." // << " With result of: " << result << endl; /* //------------------------------- PSNR GPU Optimized-------------------------------------------- time = (double)getTickCount(); // Initial call result = getPSNR_GPU_optimized(I1, I2, bufferPSNR); time = 1000*((double)getTickCount() - time)/getTickFrequency(); cout << "Initial call GPU optimized: " << time <<" milliseconds." << " With result of: " << result << endl; time = (double)getTickCount(); for (int i = 0; i < TIMES; ++i) result = getPSNR_GPU_optimized(I1, I2, bufferPSNR); time = 1000*((double)getTickCount() - time)/getTickFrequency(); time /= TIMES; cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds." << " With result of: " << result << endl << endl; //------------------------------- SSIM CPU ----------------------------------------------------- Scalar x; time = (double)getTickCount(); for (int i = 0; i < TIMES; ++i) x = getMSSIM(I1,I2); time = 1000*((double)getTickCount() - time)/getTickFrequency(); time /= TIMES; cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds." << " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl; //------------------------------- SSIM GPU ----------------------------------------------------- time = (double)getTickCount(); for (int i = 0; i < TIMES; ++i) x = getMSSIM_GPU(I1,I2); time = 1000*((double)getTickCount() - time)/getTickFrequency(); time /= TIMES; cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds." << " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl; //------------------------------- SSIM GPU Optimized-------------------------------------------- time = (double)getTickCount(); x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM); time = 1000*((double)getTickCount() - time)/getTickFrequency(); cout << "Time of MSSIM GPU Initial Call " << time << " milliseconds." << " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl; time = (double)getTickCount(); for (int i = 0; i < TIMES; ++i) x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM); time = 1000*((double)getTickCount() - time)/getTickFrequency(); time /= TIMES; cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds." << " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl; return 0; */ getchar();} |
