# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## Proposed Method
### Statistics in Color Space  
Color is also an important ingredient for visual quality perception [31]. [PERSON] et al. [32] investigated the important role that color information played in image quality prediction. [PERSON] et al. [33] utilized the color distribution features for reduced-reference IQA algorithm. [PERSON] et al. [1] proposed an unsupervised learning approach that utilized the structural information in the YCbCr color space to improve the prediction accuracy of image quality. However, only a few BIQA algorithms concerned about the effect of color information. Considering the HSV color space, which contains the three components hue, saturation, and lightness, provides an intuitive representation of color and is more suitable than the RGB color space to capture features correlate well with human perception, we employ the color entropy and the low-level statistics of the three channels in the HSV color space as a complement to the gray-scale features.  
Visual entropy can effectively measure the uncertainty of an image, and can be utilized to quantify the distorted information [34; 35]. Therefore, we employ the color entropy of the three channels in the HSV space to characterize color information. The entropy of the \(k\)th channel is calculated by  
\[E^{k}=\sum_{i=0}^{255}p_{i}^{k}\log_{2}p_{i}^{k} \tag{13}\]  
where \(p_{i}^{k}\) is the probability density of \(i\)th level in \(k\)th channel, \(k\in{1,2,3}\).  
Image gradient magnitude is sensitive to the degradations of images [19; 36; 37]. Therefore, before calculating the low-level statistics for the HSV color space, we compute the gradient magnitude map for each channel in the HSV color space. The gradient magnitude map of \(k\)th channel is computed by  
\[GM^{k}=\sqrt{[I^{k}(x,y)*G_{h}]^{2}+[I^{k}(x,y)*G_{v}]^{2}} \tag{14}\]  
where \(\star\) is the linear convolution operator, \(I^{k}(x,y)\) denotes the map of the \(k\)th channel in the HSV color space, and \(G_{h}\) and \(G_{v}\) are the Gaussian partial derivative filters applied along the horizontal (h) and vertical (v) directions. Here, the Gaussian partial derivative filter is used as convolution masks to perform a local averaging to reduce the effects of noise in the HSV color space, and can be defined as  
\[G_{d}=\frac{\partial}{\partial d}g(x,y|\sigma)=-\frac{1}{2\pi\sigma^{2}}\frac {d}{\sigma^{2}}\text{exp}\Big{(}-\frac{x^{2}+y^{2}}{2\sigma^{2}}\Big{)},d\in (h,v) \tag{15}\]  
where \(\sigma\) is the scale parameter of the Gaussian function \(g(x,y|\sigma)\).  
Figure 6 shows how the 3D scatter plots of the mean, the standard deviation, and the entropy of each channel in the HSV color space distributed in the feature space, where each point in the scatter plots represents one distorted image in LIVE database. We can see clearly that the distorted images belonging to different categories of DMOS values occupy different regions of the feature space, which  
Figure 5: Distributions of the DFMs follow the Weibull distribution, and the distribution statistics of the DFMs increase as the MOS value grows: (**a**) distributions; and (**b**) bar plots of \(a\) and \(b\).  
means that the color features extracted from the HSV color space can help to distinguish images of different quality scores.