Image Quality Assessment

Image quality is based on a visible distortion that can occur in an image. An example of quality would be blurriness, color contrast, Gaussian noise, etc. In order to assess quality in an analytical way, an accurate and meaningful as the human perception quantification of these distortions is needed. In literature, Image Quality Assessment (IQA) has been categorized into different groups. Perhaps the most popular distinction of these technique would be into two major groups: full-reference and no reference IQA.

2.4.1 Full-Reference

The full-reference IQA is in need of a reference original image to be able to compare the input image and estimate its quality based on the original one. These full-reference IQA can also be divided into sub-groups based on the manner of estimating the distortion in an image. It can be a mathematical based such as Squared Mean Error (MSE) (Tuchler et al., 2002) which is a way to assess quality relatively to the reference image by calculating the error signal (difference) and then doing its average.

MSE(x,y) = 1 N N

∑

where xi and yi are respectively the input image and the reference image. The signal error is expressed in ei= xi− yi. Another mathematical and full-reference IQA is the peak to Signal to Noise Ratio (PSNR) (Huynh-Thu and Ghanbari, 2008) which is inversely proportional to MSE.

PSNR= 10log10 L2

MSE (2.23)

Where L is the dynamic range of pixels. The advantage of PSNR is that it is useful for different dynamic range of an image.

Aside from these mathematical approach, other full-reference techniques have also been intro- duced such as the Structural similarity Index (SSIM) (Wang et al., 2004b). This method is a measure of structural change of information between the original image and the distorted one.

SSIM(x,y) = (2μxμy+C1)(2σxy+C2)

(μ2

x+ μy2+C1)(σx2+ σy2+C2)

(2.24)

whereμx,sigmax andμy,sigmayare respectively the mean intensity and standard deviation of the image x and the image y and σxy is their cross validation. As for C1 and C2 they are constants having a low value to omit the problem having the denominator going to zero. In literature full-reference IQA methods are considered to provide fidelity according to the original image instead of global quality.

2.4.2 No-Reference

No-reference IQA, also called "blind" IQA is a measure that is capable to return a quality estimation without having a reference image. Unlike the full-reference one, this type is more useful for applications where the reference images are not available. An example of these applications would be memory card management of a digital camera where this last mentioned must be able to assess which of the photos are of good quality for storage and which are not.There are a lot of techniques for no-reference IQA depending on the type of distortion. In

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this part we are going to present some facial related no-reference IQA which are : head pose, contrast and sharpness.

Head Pose Estimation

It is important to mention that IQA for head pose is divided into two types: IQA involving facial features and IQA dealing with the face as a whole. The first type consists in identifying specific features from the facial image then calculating certain measures. For example in a given facial image, the position of the eyes is located then the distance between both eyes is calculated to estimate the pose of the face. This category of pose quality does not always work very well and not reliable especially in conditions where the rotation of the head is very huge that it is hard to locate both eyes. For the second type, it deals with the face as a whole element. In (Abdel-Mottaleb and Mahoor, 2007) a QM for Head Pose is proposed. IQA involves facial features. To assess the facial image quality, three facial feature points were located: the center of the eyes and the mouth and use an algorithm for skin color discrimination for the left part

SL and the right part SR.

pose= SL− SR

min(SL,SR) (2.25)

Another head pose estimation was proposed in (Nasrollahi and Moeslund, 2008) where the whole face image is exploited instead of locating specific features as the previous technique. In order to estimate the head pose series of tasks are needed to be accomplished. The first task is to calculate and locate the coordinates of the center of the mass of the image using the equations below.

xm=∑ N

i=1∑Mj=1ib(i, j)

ym= ∑ N

i=1∑Mj=1 jb(i, j)

A (2.27)

(xm,ym) are the coordinates of the center of mass of the image. b(i, j) is the binary version of the original image I. MxN is thr size of the image abd A is the area of the detected region. Then, the next task is to locate the coordinates of the center of the face region detected.

xc= x2− x1 2 (2.28) yc= y2− y1 2 (2.29)

x1 and x2 are respectively the most right and the most left pixel of the face region. y1 and y₂ are respectively the lowest and the most top pixel of the detected face as seen in (Nasrol- lahi and Moeslund, 2008) 2.2. The knowledge of these values involves the usage of Gradient analysis in order to collect these positions from the facial image.

Once these coordinates (xm,ym) and (xc,yc) are calculated, it is now possible to assess the

Figure 2.2 Head pose estimation with center of mass (+) and center of the region (*)

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position of the head as:

pose=

(xc− xm)2+ (yc− ym)2 (2.30) The closer this value to zero the closer the face to be frontal.

Sharpness

In video surveillance, the individuals in the scene are all moving. So, it easy to have an affected image by motion blur where the facial image becomes blurred and useless for further treatment. It is then obvious to define a sharpness feature. In (Weber, 2006), the approach consists of applying a low-pass filter to the facial image first. Afterwards, calculate the sharpness of the face by calculating the average value of pixels of the original image I(x,y) and the filtered image l f I(x,y).

shar pness= |l f I(x,y) − I(x,y)| (2.31) The higher this value the better and the sharper the image is.

Sharpness is also defined in the QM standards for facial images ISO/IEC (Sang et al., 2009). The QM is based on the evaluation of the frequency domain DCT. First thing to be done is to apply the IDCT operation on the original image R(x,y). So the sharpness would be expressed as the equation 2.32 shar pness= 1 M× N

∑

∑

Contrast is an important measure to distinguish relative differences in terms of the intensity of an image. An expression of this image quality is given in (Abaza et al., 2012).

CRMS=

∑M

x=1∑Ny=1[I(x,y) − μ]2

CRMSis the contrast value. I(x,y) is a test image of size M ×N. μ is the mean value of intensity of the image. Imin and Imaxare respectively the minimum and maximum intensity values.

Illumination/Brightness

Images that are poor in illumination tend to be useless for further processing especially for recognition. This subsection provides a simpler way to assess brightness of an image without the use of any reference image.

This was highlighted and used by (Nasrollahi and Moeslund, 2008). They assumed that the region of the image is small and that the average value of the pixels values of that region is the illumination measure.