# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 4 Experimental Results  
In the field of IQA, indeed, there are many public databases, such as TID2013 [20] and KADID-10K [21], CID2013 [22], LIVE challenge [23], and KonIQ-10K [24]. However, these databases are either synthetically or authentically distorted IQA databases. We aim at assessing the retargeted images database in this paper, so we choose a popular public database [3] for retargeted images to validate the proposed algorithm. The database contains 37 source reference images and corresponding retargeted images. Two hundred and ten participants take part in the assessment of subjective image quality. All the data constitute this database. Every source reference image has been retargeted by eight different models, including MO, CR, SV, SM, SC, SCL, SNS, and WARP. Furthermore, subjective image evaluation results, for all retargeted images using above models, are also provided in this database. Figure 2 shows an example of child image about a source reference image and eight retargeted images with different models. Figure 3 shows the histogram of subjective and objective assessment. The left is the subjective votes for the child image in Figure 2 by participants, and the right is objective results by our proposed algorithm on the same image. For the image obtained by each retargeted model, every participant gives a score by observing the image itself, and the score range is [0, 100]. Then, the subjective score is the mean value of all participants' scores. The objective score is calculated by our proposed algorithm in Equation (6). There is no specific objective score range, and the score is the sum of local EMD and global EMD. Therefore, both histograms are in different scales. Because our algorithm is based on EMD, which is the distance between two image patches, the smaller value of objective result, the closer the distance, and the more similarities between the two images. When calculating the correlation, we only calculate the consistency of subjective and objective scores, and we do not evaluate the values of the two scores. The existing retargeted IQA algorithms mainly based on full reference, while existing RR IQA algorithms are often used for some special distorted images, such as noise, blur, and JPG, not for retargeted images, so it is hard to compare our proposed algorithm with them directly. Therefore, we separate all the compared IQA algorithms into two types in the experiment. One is full reference IQA algorithms for retargeted images, including CL, EH, BDW, BDS, and SIFT Flow; the other is RR IQA algorithms for non-retargeted images, including MA, VIF, DNT, RRED, and RRSSIM. Four criteria [8] are used to assess how well the objective quality scores can predict the subjective quality scores: (1) [PERSON] linear correlation coefficient (PLCC), (2) [PERSON]'s rank correlation coefficient (SRCC), (3) [PERSON]'s rank correlation coefficient (KRCC), and (4) Root mean squared (RMS) error. In the experiment, for each reference image and a series of retargeted images, the subjective rankings can be obtained from the database, and the different objective rankings can be computed by different IQA algorithms, so those algorithms can be compared by the subjective and objective correlation. To better analyze the effectiveness of all the algorithms for different types of image assessment, 37 images in the database are divided into 6 types according to the selected attributes: 25 lines or edges images, 15 faces or people images, 6 texture images, 18 foreground objects images, 16 geometric structures images, and 6 symmetry images. Note that one image may belong to several different types, since it can contain several attributes, such as faces and people, which often belong to the foreground objects images. Table 1 presents these correlation scores of subjective and objective measures for the KRCC according to image attributes. As expected, the FR IQA for retargeted images  
Figure 3: Histogram of subjective and objective assessment. (**left**) Subjective votes for the child image by participants.