本文介绍了基于图像结构相关性的经典质量评估方法SSIM和MS-SSIM。SSIM通过亮度、对比度、结构三个模块计算图像相似度；MS-SSIM则是多尺度的SSIM。文中还提及Paddle的实现，包括paddle_msssim包的使用，测试对比显示其与其他实现结果接近且速度有优势，并给出了计算指标和作为损失函数的示例。
☞☞☞AI 智能聊天, 问答助手, AI 智能搜索, 免费无限量使用 DeepSeek R1 模型☜☜☜
SSIM 研究主页
SSIM 论文
MS-SSIM 论文
Paddle-MSSSIM
SSIM（Structural SIMilarity）即结构相似性指数，是一种测量两个图像之间相似性的方法
假定其中一幅图像具有完美的质量，则 SSIM 指数可以被视为另一幅图像质量的度量。
SSIM 指数的计算流程如下图所示：
由 SSIM 测量系统可得相似度的测量可由三种对比模块组成，分别为：亮度（l），对比度（c），结构（s），各个模块的计算公式如下：
总体的计算公式如下：
MS-SSIM（Multi-Scale Structural Similarity）即多尺度结构相似性指数
是一种基于多尺度（图片按照一定规则，由大到小缩放）的 SSIM 指数
具体的计算公式如下：
基于 Pytorch MS-SSIM 项目开发了一个快速、可微分的 SSIM 和 MS-SSIM 的 Paddle 实现
可以通过安装并调用 paddle_msssim 包快速实现 SSIM 和 MS-SSIM 的计算
Paddle MS-SSIM 与 SKImage、TensorFlow 和 Pytorch MS-SSIM 实现的测试对比结果如下：
outputs(AMD Ryzen 4600H):
===================================
Test SSIM
===================================
====> Single Image
Repeat 10 timessigma=0.0 ssim_skimage=1.000000 (247.7732 ms), ssim_tf=1.000000 (277.2696 ms), ssim_paddle=1.000000 (179.4677 ms), ssim_torch=1.000000 (183.6994 ms)sigma=10.0 ssim_skimage=0.932399 (226.1620 ms), ssim_tf=0.932640 (257.2435 ms), ssim_paddle=0.932636 (163.2263 ms), ssim_torch=0.932400 (179.1418 ms)sigma=20.0 ssim_skimage=0.786023 (224.1826 ms), ssim_tf=0.786032 (279.2126 ms), ssim_paddle=0.786017 (158.3070 ms), ssim_torch=0.786027 (180.0890 ms)sigma=30.0 ssim_skimage=0.637174 (237.5582 ms), ssim_tf=0.637183 (267.6092 ms), ssim_paddle=0.637165 (167.9277 ms), ssim_torch=0.637178 (181.7910 ms)sigma=40.0 ssim_skimage=0.515865 (221.0388 ms), ssim_tf=0.515876 (264.3230 ms), ssim_paddle=0.515857 (170.7676 ms), ssim_torch=0.515869 (189.0941 ms)sigma=50.0 ssim_skimage=0.422551 (222.6846 ms), ssim_tf=0.422558 (273.1971 ms), ssim_paddle=0.422542 (168.3579 ms), ssim_torch=0.422554 (176.7442 ms)sigma=60.0 ssim_skimage=0.351337 (215.1536 ms), ssim_tf=0.351340 (270.5560 ms), ssim_paddle=0.351325 (164.3315 ms), ssim_torch=0.351340 (194.6781 ms)sigma=70.0 ssim_skimage=0.295752 (210.0273 ms), ssim_tf=0.295756 (272.1814 ms), ssim_paddle=0.295744 (169.3864 ms), ssim_torch=0.295755 (178.9230 ms)sigma=80.0 ssim_skimage=0.253164 (239.2978 ms), ssim_tf=0.253169 (260.8894 ms), ssim_paddle=0.253157 (184.7061 ms), ssim_torch=0.253166 (181.4640 ms)sigma=90.0 ssim_skimage=0.219240 (224.7329 ms), ssim_tf=0.219245 (270.3727 ms), ssim_paddle=0.219235 (172.3580 ms), ssim_torch=0.219242 (180.5838 ms)sigma=100.0 ssim_skimage=0.192630 (238.8582 ms), ssim_tf=0.192634 (261.4317 ms), ssim_paddle=0.192624 (166.0294 ms), ssim_torch=0.192632 (175.7241 ms)
Pass!
====> Batch
Pass!===================================
Test MS-SSIM
===================================
====> Single Image
Repeat 10 timessigma=0.0 msssim_tf=1.000000 (534.9398 ms), msssim_paddle=1.000000 (231.7381 ms), msssim_torch=1.000000 (257.3238 ms)sigma=10.0 msssim_tf=0.991148 (525.1758 ms), msssim_paddle=0.991147 (213.8527 ms), msssim_torch=0.991101 (243.9299 ms)sigma=20.0 msssim_tf=0.967450 (523.3070 ms), msssim_paddle=0.967447 (217.2415 ms), msssim_torch=0.967441 (253.1073 ms)sigma=30.0 msssim_tf=0.934692 (538.5145 ms), msssim_paddle=0.934687 (215.2203 ms), msssim_torch=0.934692 (242.5429 ms)sigma=40.0 msssim_tf=0.897363 (558.0346 ms), msssim_paddle=0.897357 (219.1107 ms), msssim_torch=0.897362 (249.1027 ms)sigma=50.0 msssim_tf=0.859276 (524.8582 ms), msssim_paddle=0.859267 (232.4189 ms), msssim_torch=0.859275 (263.1328 ms)sigma=60.0 msssim_tf=0.820967 (512.8726 ms), msssim_paddle=0.820958 (223.7422 ms), msssim_torch=0.820965 (251.9713 ms)sigma=70.0 msssim_tf=0.784204 (529.6149 ms), msssim_paddle=0.784194 (213.1742 ms), msssim_torch=0.784203 (244.9676 ms)sigma=80.0 msssim_tf=0.748574 (545.3014 ms), msssim_paddle=0.748563 (222.8581 ms), msssim_torch=0.748572 (261.0413 ms)sigma=90.0 msssim_tf=0.715980 (538.3886 ms), msssim_paddle=0.715968 (214.4464 ms), msssim_torch=0.715977 (282.6247 ms)sigma=100.0 msssim_tf=0.683882 (540.9150 ms), msssim_paddle=0.683870 (218.5596 ms), msssim_torch=0.683880 (244.1856 ms)
Pass
====> Batch
Pass具体的安装使用方法如下：
!pip install paddle_msssim
这里使用如下三张图像来计算他们之间的 SSIM 和 MS-SSIM 指标，结果如下：
| Image | @@##@@ | @@##@@ | @@##@@ |
|---|---|---|---|
| Simga | 0 | 50 | 100 |
| SSIM | 1.000000 | 0.422927 | 0.192567 |
| MS-SSIM | 1.000000 | 0.858861 | 0.684299 |
具体的计算代码如下：
import cv2import paddlefrom paddle_msssim import ssim, ms_ssimdef imread(img_path):
img = cv2.imread(img_path) return paddle.to_tensor(img.transpose(2, 0, 1)[None, ...], dtype=paddle.float32)
simga_0 = imread('./images/simga_0.png')
simga_50 = imread('./images/simga_50.png')
simga_100 = imread('./images/simga_100.png')
ssim_0 = ssim(simga_0, simga_0)
ssim_50 = ssim(simga_0, simga_50)
ssim_100 = ssim(simga_0, simga_100)print('[SSIM] simga_0: %f simga_50: %f simga_100: %f' % (ssim_0, ssim_50, ssim_100))
ms_ssim_0 = ms_ssim(simga_0, simga_0)
ms_ssim_50 = ms_ssim(simga_0, simga_50)
ms_ssim_100 = ms_ssim(simga_0, simga_100)print('[MS-SSIM] simga_0: %f simga_50: %f simga_100: %f' % (ms_ssim_0, ms_ssim_50, ms_ssim_100))[SSIM] simga_0: 1.000000 simga_50: 0.422927 simga_100: 0.192567 [MS-SSIM] simga_0: 1.000000 simga_50: 0.858861 simga_100: 0.684299
import osimport sysimport paddleimport numpy as npfrom PIL import Imagefrom paddle.optimizer import Adamfrom paddle_msssim import SSIM, MS_SSIM
loss_type = 'ssim'assert loss_type in ['ssim', 'msssim']if loss_type == 'ssim':
loss_obj = SSIM(win_size=11, win_sigma=1.5, data_range=1, size_average=True, channel=3)else:
loss_obj = MS_SSIM(win_size=11, win_sigma=1.5, data_range=1, size_average=True, channel=3)
np_img1 = np.array(Image.open("./images/simga_0.png"))
img1 = paddle.to_tensor(np_img1.transpose(2, 0 , 1)).unsqueeze(0) / 255.0img2 = paddle.rand(img1.shape)
img1 = paddle.to_tensor(img1, stop_gradient=True)
img2 = paddle.to_tensor(img2, stop_gradient=False)with paddle.no_grad():
ssim_value = loss_obj(img1, img2).item() print("Initial %s: %f:" % (loss_type, ssim_value))
optimizer = Adam(parameters=[img2], learning_rate=0.05)
step = 0while ssim_value < 0.9999:
step += 1
optimizer.clear_grad()
loss = loss_obj(img1, img2)
(1 - loss).backward()
optimizer.step()
ssim_value = loss.item() if step % 10 == 0: print('step: %d %s: %f' % (step, loss_type, ssim_value))
img2_ = (img2 * 255.0).squeeze()
np_img2 = img2_.detach().numpy().astype(np.uint8).transpose(1, 2, 0)
results = Image.fromarray(np.concatenate([np_img1, np_img2], 1))
results.save('results_%s.png' % loss_type)
resultsInitial ssim: 0.010401: step: 10 ssim: 0.225660 step: 20 ssim: 0.733606 step: 30 ssim: 0.919254 step: 40 ssim: 0.970057 step: 50 ssim: 0.990348 step: 60 ssim: 0.998122 step: 70 ssim: 0.999767
<PIL.Image.Image image mode=RGB size=1024x768 at 0x7F157A7B6F50>
以上就是图像质量与相似度评估指标 SSIM 和 MS-SSIM 的 Paddle 实现的详细内容，更多请关注php中文网其它相关文章！
每个人都需要一台速度更快、更稳定的 PC。随着时间的推移，垃圾文件、旧注册表数据和不必要的后台进程会占用资源并降低性能。幸运的是，许多工具可以让 Windows 保持平稳运行。
广告Copyright 2014-2025 https://www.php.cn/ All Rights Reserved | php.cn | 湘ICP备2023035733号
559
收藏
