# 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  
Research on applied media aesthetics demonstrates that the effects of production techniques on viewers' aesthetic reflexes can be evaluated computationally, based on the aesthetic elements of multimedia items [24]. For example, [24] described how the basic elements of media aesthetics can be used to produce maximally effective visual appeal of images and videos. Another study on multimedia services demonstrated filtering of large information spaces to classify the attractiveness of multimedia items to users [25]. Other researchers have recommended the use of automatically extracted low-level features in hybrid and autonomous recommender systems [26; 27]. We hypothesize that the knowledge of individuals' subjective responses to multimedia content, based on low-level features from applied media aesthetics [24] and recommender systems [26; 27] can be extended to multimedia exposure. The elements of media aesthetics refer to the use of light, colors, screen area, depth, and volume in the three-dimensional realm, time, and movement in videos. The expressive communication of movie producers is based on established design rules, also known as movie grammar. Ref. [28] identified the elements of movie grammar using computable video features to identify the movie genre based on video previews. Their proposal included lightning key, color variance, motion content, and average shot length features along with a well-described algorithm for determining each feature. An improved proposal for aesthetically inspired descriptors applied to a movie recommendation system was presented by [PERSON] et al. [26] and is called _stylistic video features_. We adopted a basic set of visual features related to color, illuminance, object motion, and shot tempo of the video sequence, as described below. Lighting Key is a powerful tool used by directors to control the emotions evoked by video consumers [26; 28]. The use of _high-key lighting_, where less contrast is used, resulting in bright images, promotes positive and optimistic responses. The use of dark shading, resulting in low mean and standard deviation grayscale, creates a mysterious and dramatic mood typical of nor movies; this is known as _low-key lighting_. The Lighting Key \(\xi_{q}\) of a frame is defined as the product of the mean \(\mu\) and standard deviation \(\sigma\) of the brightness component (\(V\)) in the three-dimensional color space \(HSV\),  
\[\xi_{q}=\mu\cdot\sigma. \tag{2}\]  
Typically, high values of both the mean and standard deviation of brightness are associated with funny video scenes, which may be associated with \(\xi_{q}>\tau_{c}\). On the other hand, dark, dramatic scenes indicate the opposite situation, where \(\xi_{q}<\tau_{h}\). In some video materials, where \(\tau_{h}<\xi_{q}<\tau_{c}\) lighting key is less pronounced, which directly affects viewer mood [26]. The Lighting Key value for an entire video is defined as the average of \(\xi_{q}\). To speed up the calculations, we can take the middle frame of each shot as a representative value, resulting in  
\[\mu_{lk}=\frac{\sum_{q=1}^{n_{sh}}\xi_{q}}{n_{sh}}. \tag{3}\]  
Color Variance is another indicator closely related to the mood that a director intends to express with the video [26; 28]. Color Variance is a scalar dimension that indicates how vibrant a movie's color palette is. Lighter colors are generally used in light and funny videos, whereas darker colors are predominant in horror and morbid scenes. We can calculate the Color Variance in the three-dimensional color space \(Luv\) using the covariance matrix of the three color dimensions. The value of the Color Variance per frame is equal to  
\[\Sigma_{q}= \begin{vmatrix}\sigma_{L}^{2}&\sigma_{lu}^{2}&\sigma_{lu}^{2}\\ \sigma_{lu}^{2}&\sigma_{u}^{2}&\sigma_{uw}^{2}\\ \sigma_{Lv}^{2}&\sigma_{uv}^{2}&\sigma_{v}^{2}\end{vmatrix}