# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work
### Video and Multimedia Content Descriptors
#### 2.2.2 Stylistic Video Features
 \tag{4}\]  
The Final Color Variance value for the entire video is the average of \(\Sigma_{q}\) using the representative key frames of each shot, resulting in  
\[\mu_{cv}=\frac{\sum_{q=1}^{n_{sh}}\Sigma_{q}}{n_{sh}} \tag{5}\]  
Motion Activity within a video refers to movements of the camera or objects within the scene. The shot and cut dynamics are also included to a certain degree in the Average Shot Length video features described in the next subsection. However, object dynamics remain an independent feature related to viewers' feelings evoked by videos. Some early studies used MPEG motion vectors in the compression domain to obtain motion information of a video [29]. Ref. [28] uses a method based on _visual disturbance_ of the image sequence. Ref. [26] proposes to characterize motion activity using optical flow [30], which indicates the velocity of objects in a sequence of images. According to their proposal, both the mean value of the pixel motions  
\[\mu_{\overline{m}}=\frac{\sum_{t=1}^{n_{f}}\overline{m}_{t}}{n_{f}} \tag{6}\]  
and standard deviation of pixel motions  
\[\mu_{\sigma_{m}^{2}}=\frac{\sum_{t=1}^{n_{f}}(\sigma_{m}^{2})_{t}}{n_{f}} \tag{7}\]  
can be applied as motion features of a video. Average Shot Length is defined as the average of the shot duration  
\[\overline{L_{sh}}=\frac{n_{f}}{n_{sh}}, \tag{8}\]  
where \(n_{f}\) is the number of frames and \(n_{sh}\) is the number of shots in the entire video being evaluated. This ratio provides a good estimate of the tempo of the scenes, which is controlled by the video director. This feature was introduced and evaluated in depth by [31] and was later used by [26; 28]. There are many algorithms for automatic scene transition detection that are well described in [28; 31]. Well-known techniques for scene detection include content-based, adaptive-, and threshold-based scene detectors. These three techniques are used in an open-source scene detection tool _PySceneDetect_ hosted on GitHub [32]. The content-aware method detects jump cuts within a video and is triggered when the difference between two consecutive frames exceeds a predefined threshold. It operates in the HSV color space across all three color channels. The adaptive method works in a similar manner but uses the moving average of several adjacent changes to reduce the detection of false scene transitions in the case of fast camera movements. The threshold method is the most traditional method which, according to PySceneDetect, triggers a scene cut when the brightness of an image falls below a predefined threshold (fade-out). The five presented video features represent a good estimate of the stylistic video features and together they form a video feature vector  
\[\mathbf{f_{v}}=(\mu_{lk},\mu_{cv},\overline{L_{sh}},\mu_{\overline{m}},\mu_{ \varphi_{\overline{m}}^{2}}) \tag{9}\]  
comprised of Lighting Key, Color Variance, Average Shot Length, Mean Motion Activity and Standard Deviation of Motion Activity. At this early stage of development, we decided to use more explanatory and compact stylistic video features rather than MPEG-7. The above feature vector \(\mathbf{f_{y}}\) was therefore used in our study. A collection of related work findings is assembled in Table 2.