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We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos.
We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind Image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification.
We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems.
The paper studies the community detection problem on Telegram channels. The dataset is received from TGStat service and includes the information of 58k forwards between 100 politician Telegram channels. We implement modern clustering approaches to solve the problem of missing social links. Our study is based on a combination of structural features with strategy-based attributes, including indicators designed according to the nodes’ role in a network. Authors provide ten novel indicators, which are calculated for each network’s member per each message in order to vectorize a Telegram channel with regard to its strategy of information spread and the way of contacting other channels. Authors construct a metric-based graph of channel relations and cluster channels representations using network science techniques. Obtained results are studied using quantitative and qualitative analysis showing promising results in applying joint network-based and KPI-based models for the stated problem.
Nowadays there are representative volumes of demographic data which are the sources for extraction of demographic sequences that can be further analysed and interpreted by domain experts. Since traditional statistical methods cannot face the emerging needs of demography, we used modern methods of pattern mining and machine learning to achieve better results. In particular, our collaborators, the demographers, are interested in two main problems: prediction of the next event in a personal life trajectory and finding interesting patterns in terms of demographic events for the gender feature.
The main goal of this paper is to compare different methods by accuracy for these tasks. We have considered interpretable methods such as decision trees and semi- and non-interpretable methods, such as the SVM method with custom kernels and neural networks. The best accuracy results are obtained with a two-channel convolutional neural network. All the acquired results and the found patterns are passed to the demographers for further investigation.