2015BIQA论文纵览（二）

1. Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment**重点内容**
   the natural images to exhibit redundant information over various scales
   low pass error、high pass error
   uses the intrinsic global change of the query image across scales
   这个方法也是opinion-unaware的，而且不需要训练模型，只是从失真图像本身各尺度信息的改变来评价质量

2. The Application of Visual Saliency Models in Objective Image Quality Assessment:A Statistical Evaluation
   视觉统计模型在客观图像质量评价中的应用：统计评估

3. Tasking on Natural Statistics of Infrared Images
   红外图像自然统计工作

4. Image Quality Assessment Using Human Visual DOG Model Fused With Random Forest
   利用人类视觉高斯差异模型融合随机森林评价图像质量

5. Geometrical and Statistical Properties of Vision Models obtained via Maximum Differentiation
   基于最大变异的视觉模型几何和统计性质的获得

6. Objective Quality Assessment for Color-to-Gray Image Conversion
   彩色转灰度变换图像的客观质量评价
   we propose a C2G structural similarity (C2G-SSIM) index
   Inspired bythe philosophy of the structural similarity index
   真是好灵感啊，作者该去研究哲学或者神学

7. No-reference image quality assessment with shearlet transform
   and deep neural networks
   基于剪切波变换和深度神经网络的无参考图像质量评价
   features are extracted by a new multiscale directional transform (shearlet transform) and the sum of subband coefficient amplitudes (SSCA)
   stacked autoencoders make primary features more discriminative.
   以shearlet变换后子代系数振幅和为特征，利用堆栈自编码放大差异，建立深度神经网络模型评价图像质量

8. Perceptual image quality assessment by independent feature detector
   利用独立特征探测器评价图像质量

9. Objective Quality Assessment for Multiexposure Multifocus Image Fusion
   多次聚焦多电极对焦融合图像的客观质量评价
   1) contrast preservation; 2) sharpness; and3) structure preservation
   create an image fusion database
   利用对比保存、清晰度、结构保存三方面评价多次曝光、对焦图像的质量

10. Objective Quality Assessment of Interpolated Natural Images
    以内插值替换的自然图像客观质量评价
    adopts a natural scene statistics (NSS) framework
    deviation of its statistical features from the NSS models trained upon high-quality natural images
    interpolated natural image distortion (IND) and weighted IND
    图像内插技术广泛用于将低分辨率图像变成高分辨率图像，本文提出了一种评价内插后图形质量的方法，通过NSS框架来评价待测图像，比较提出的两种失真情况