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Comparing CNNs and JPEG for Real-Time Multi-view Streaming in Tele-Immersive Scenarios

Konstantoudakis, Konstantinos; Christakis, Emmanouil; Drakoulis, Petros; Doumanoglou, Alexandros; Zioulis, Nikolaos; Zarpalas, Dimitrios; Daras, Petros


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  <dc:creator>Konstantoudakis, Konstantinos</dc:creator>
  <dc:creator>Christakis, Emmanouil</dc:creator>
  <dc:creator>Drakoulis, Petros</dc:creator>
  <dc:creator>Doumanoglou, Alexandros</dc:creator>
  <dc:creator>Zioulis, Nikolaos</dc:creator>
  <dc:creator>Zarpalas, Dimitrios</dc:creator>
  <dc:creator>Daras, Petros</dc:creator>
  <dc:date>2018-11-26</dc:date>
  <dc:description>Deep learning-based codecs for lossy image compression have recently managed to surpass traditional codecs like JPEG and JPEG 2000 in terms of rate-distortion tradeoff. However, they generally utilize architectures with large numbers of stacked layers, often making their inference execution prohibitively slow for time-sensitive applications. In this work, we assess the suitability of such compression techniques in real-time video streaming, and, more specifically, next-generation interactive tele-presence applications, which impose stringent latency requirements. To that end, we compare a recently published work on image compression based on convolutional neural networks which achieves state-of-the-art compression ratio using a relatively lightweight architecture, against a CPU and a GPU implementation of JPEG, measuring compression ratios and timings. With these results, we run a simulation of a tele-immersion pipeline for various networking conditions and examine the performance of the compared codecs, calculating framerates and latencies for different codec/network combinations.</dc:description>
  <dc:identifier>https://zenodo.org/record/3137854</dc:identifier>
  <dc:identifier>10.1109/SITIS.2018.00022</dc:identifier>
  <dc:identifier>oai:zenodo.org:3137854</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/761699/</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Video</dc:subject>
  <dc:subject>Compression</dc:subject>
  <dc:subject>Tele-Immersion</dc:subject>
  <dc:subject>3D Media Streaming</dc:subject>
  <dc:subject>Performance Evaluation</dc:subject>
  <dc:title>Comparing CNNs and JPEG for Real-Time Multi-view Streaming in Tele-Immersive Scenarios</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
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