# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 4 Discussion  
We proposed a method for image quality evaluation of specialized content images. This quality score is required to be able to point to the best-quality image from the set of images. The problem of choosing the best quality images appears during the photogrammetric reconstruction of the human 3D head model when images are collected by making a video of your own or someone else's head. In this case, some frames are redundant and can be dropped to speed-up reconstruction. Some frames can be motion blurred when the lighting is dimmer and the camera moves faster. Therefore, we would like to pick frames that are the least motion-blurred. A simple solution would be to split the frame sequence into chunks and take a single best-quality frame from each chunk. This procedure would reduce data redundancy as they were covering a small region in space and improve data quality as better quality images were left. The described reduction of frame count will lead to faster and more accurate reconstruction of the head 3D model. The proposed method reuses features from one of the early convolutional neural network (CNN) layers. This CNN already has its purpose in 3D head reconstruction--it detects the human head. Head detection (in general--object detection) allows for the head's background to be masked. Background masking is required when it is not static and consequently deteriorates and slows down the photogrammetric reconstruction workflow. So there is no overhead in extracting required features. Experiments showed that early features of CNN are useful in scoring images according to their quality. We chose to infer some image quality scores as the inverse of motion blur level. Because images were collected via single-take video, many of the image quality-defining factors are fixed: same camera optics, camera sensor, environmental video-taking conditions, and camera settings (ISO, shutter speed). The one factor that strongly influences image quality and in most cases changes is motion blur. It depends on the speed of camera movement, and it is not simple to guarantee the camera's stable and adequately slow movement while making a video with a smartphone by hand. So the best quality image in the image set is the image with the least motion blur. The proposed method for image quality evaluation is based on inferring motion blur level from the early features of a CNN-based head detector, using a LightGBM ranker model. The model was trained on synthetically expanded (augmented) real images of a mannequin's head--augmentation simulated motion blur of various levels and directions. From the one original image via augmentation, four additional images of increasing motion  
Figure 5: Graphical summary of all the experimental results of methods comparison. The results are the percentage of correct selections of a less motion-blurred image from a pair of images. Images of a mannequin head and of real faces were used. The chart gives the average performance of the methods tested using all image pairs (overall) and harder image pairs. Harder image pairs are image pairs that are the most similar according to the motion blur level. blur lengths were generated. These images were passed to CNN for head detection, and features (activations) from the first ten convolutional layers were collected for the training of the LightGBM ranker model. Features from separate feature maps were used with separate models, so we compared the usefulness of features from different feature maps. Absolute values of image blurriness are unknown as the ground truth blurriness of the initial image is unknown. The following images, which were generated from the original by simulating motion blur of random direction but fixed length, can be ordered in increasing order of blurriness. So the solution to train the machine learning model was to train the LightGBM ranker model. It does not require an absolute ground truth value of the image property that should be predicted, but only a ranking order.