在网上下的ssim，翻译修改后，读取图片应当相差不大，在main函数中运行即可。

图像质量的客观评价是指用畸变图像偏离原始图像的误差来衡量畸变图像的质量，目前人们最常用的指标是PSNR（李英明，2011，中国图象图形学报）设 和 分别表示原始图像和待评价图像，且有 PSNR值越大，表示畸变图像 和原始图像 越接近，视觉感知越好

用于图像质量评价，该代码是matlab版的

图像质量评价方法代码matlab代码matlab-mylib 这个库是自己在做图像、视频质量评价（I/VQA）实验时需要经常用到的函数的汇总，函数部分为手动实现，部分为其他论文里的代码（均已表明出处）。 lib一直更新。 mscn.m 【论文[1]实现的方法】 一种归一化方法，在BRISQUE算法中被提出。 [1] Mittal, A., Moorthy, AK, & Bovik, AC (2012). No-reference image quality assessment in the spatial domain.IEEE Transactions on Image Processing, 21(12), 4695-4708. SDSP.m 【论文[2]实现的方法】 LinZhang的算法中使用的求图像显着性的方法。 [2] L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: A feature similarity index for image quality assessment,” IEEE Trans. Image Pro

图像质量评价数据库TID2013 网盘下载-附件资源

基于图像融合的多种图像质量评价算法，评价指标包括MI,Qabf,FMI_pixel,FMI_dct,FMI_w,Nabf,SCD,SSIM, MS_SSIM

代码运行效果图见压缩包

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, the acquisition of image quality scores has several limitations: 1) scores are not precise, because subjects are usually uncertain about which score most precisely represents the perceptual quality of a given image; 2) subjective judgments of quality may be biased by image content; 3) the quality scales between different distortion categories are inconsistent, because images corrupted by different types of distortion are evaluated independently in subjective experiments; and 4) it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting sufficient images associated with different kinds of distortion that have diverse levels of degradation, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as “the quality of image Ia is better than that of image Ib” for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance

图像质量评价可有效评估图像采集和传输过程引起的失真或退化，在数字多媒体领域具有广阔的应用前景，无参考图像质量评价算法由于不需要参考图像先验知识，近年来成为图像质量评价领域研究的热点。在对国内外文献进行广泛调研的基础上，从评价算法原理和性能比较两个方面，系统综述了BIQI、DIIVINE、BLIINDS、BLIINDS-II、BRISQUE、NIQE和GRNN等当前性能较优的几种无参考图像质量评价算法。介绍了各种算法的特征提取和质量评价原理，在LIVE数据库上对上述评价方法进行仿真评估，并分析和比较了各种算法的评价性能和执行速度，提出了无参考评价方法的进一步研究方向。综述的几种无参考图像质量评价算法虽然已具有很好的效果，但在评价时严重依赖数据库中的主观评价数据，并且在评价精度和算法复杂度方面还存在一些不足，需要进行深入研究。

融合图像质量评价综述融合图像质量评价综述融合图像质量评价综述融合图像质量评价综述融合图像质量评价综述融合图像质量评价综述融合图像质量评价综述