Description
In this work, we implement multiple techniques for predicting users viewing directions while watching 360° videos. We utilize historical viewing traces to forecast future directions based on a real-life head tracking dataset. We compare the performance of linear regression (LR), artificial neural networks (ANN), long short-term memory (LSTM), and convolutional neural networks (CNN). We assess their efficiency in terms of viewing angles prediction errors. We also investigate tile viewing prediction in tile-based 360° video transmission scenarios. We built two classifiers based on ANN and LSTM to predict watched tiles and provide an evaluation of their performance in this article.Period | 28 Mar 2024 |
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Event title | 2024 Panhellenic Conference on Electronics and Telecommunications |
Event type | Conference |
Location | Thessaloniki, GreeceShow on map |
Degree of Recognition | International |
Documents & Links
Related content
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Research output
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A Comparative Analysis of Viewing Prediction Techniques for 360° Video Streaming Applications
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review