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Hamed Ahmadi: a content-aware bitrate controller for cloud gaming

Hamed Ahmadi: a content-aware bitrate controller for cloud gaming PhD Thesis Summaries Email: christian.timmerer@itec.aau.at | christian.timmerer@bitmovin.com thesis we work on them. Doing so, two datasets and two perceptual models are proposed. The first dataset includes a variety of video games and their objects. The model built on this dataset is grounded on visual attention mechanism and predicts the player's gaze location based on a combination of low level signal properties and game object prioritization. Experimental results show that this model decreases the required bit rate by nearly 25% on average, while maintaining a relatively high user quality of experience. The second perceptual model, addresses the difference among attention patterns of the players. To develop this model, the recorded eye-tracking data is first clustered. Then, the correlation of clusters and skill levels are shown via statistical and experimental methods. Our analyses show that this model decreases the bandwidth by up to 15% based on the player's skill. The second step is to incorporate the perceptual models into the video encoder by means of perceptual rate-distortion models to assign bits to each region of the video according to its importance to HVS. Since current attention-based bit allocation algorithms do not take other HVS properties into account, in some cases http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMultimedia Records Association for Computing Machinery

Hamed Ahmadi: a content-aware bitrate controller for cloud gaming

ACM SIGMultimedia Records , Volume 8 (2) – Aug 2, 2016
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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1947-4598
DOI
10.1145/2982857.2982861
Publisher site
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Abstract

PhD Thesis Summaries Email: christian.timmerer@itec.aau.at | christian.timmerer@bitmovin.com thesis we work on them. Doing so, two datasets and two perceptual models are proposed. The first dataset includes a variety of video games and their objects. The model built on this dataset is grounded on visual attention mechanism and predicts the player's gaze location based on a combination of low level signal properties and game object prioritization. Experimental results show that this model decreases the required bit rate by nearly 25% on average, while maintaining a relatively high user quality of experience. The second perceptual model, addresses the difference among attention patterns of the players. To develop this model, the recorded eye-tracking data is first clustered. Then, the correlation of clusters and skill levels are shown via statistical and experimental methods. Our analyses show that this model decreases the bandwidth by up to 15% based on the player's skill. The second step is to incorporate the perceptual models into the video encoder by means of perceptual rate-distortion models to assign bits to each region of the video according to its importance to HVS. Since current attention-based bit allocation algorithms do not take other HVS properties into account, in some cases

Journal

ACM SIGMultimedia Records – Association for Computing Machinery

Published: Aug 2, 2016

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