This work introduced a novel analysis method to estimate interaction intensity, i.e., the level of positivity/negativity of interaction, for intimate couples (married and heterosexual) under the impact of alcohol, which has great influences on behavioral health. Non-verbal behaviors are critical in interpersonal interactions. However, whether computer vision-detected non-verbal behaviors can effectively estimate the interaction intensity of intimate couples is still unexplored. In this work, we proposed novel measurements and investigated their feasibility to estimate interaction intensities through machine learning regression models. Analyses were conducted based on a conflict-resolution conversation video dataset of intimate couples before and after acute alcohol consumption. Results showed the estimation error was at the lowest in the no-alcohol state but significantly increased if the model trained using no-alcohol data was applied to after-alcohol data, indicating that alcohol altered the interaction data in the feature space. While training a model using rich after-alcohol data is ideal to address the performance decrease, data collection in such a risky state is challenging in real life. Thus, we proposed a new State-Induced Domain Adaptation (SIDA) framework, which allows for improving estimation performance using only a small after-alcohol training dataset, pointing to a future direction of addressing data scarcity issues.

Compared to ordinary images, omnidirectional image (OI) usually has a broader view and a higher resolution, and image quality assessment (IQA) can help people to understand and improve their visual experience. However, the current IQA works cannot achieve good performance. To address this, we proposed a novel visual perception-based no-reference/blind omnidirectional image quality assessment (NR/B-OIQA) model. The gradient-based global structural features and gray-level co-occurrence matrix-based local structural features are combined together to highlight the rich quality-aware structural information. And a novel steganalysis real model-based color descriptor is extracted to reflect the color information that ignored in most IQA models. With a multi-scale visual perception, we take image entropy and the natural scene statistics features to convey the high-level semantics and quantify the unnaturalness of omnidirectional images. Finally, we apply support vector regression to predict the objective quality value based on the subjective scores and extracted all features. Experiments are conducted on OIQA and CVIQD2018 Databases, and the results illustrate that our model has more reliable performance and stronger competitiveness and receives better conformity with the subjective values.