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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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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>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.</p>", 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@type": "Person", 
      "name": "Konstantoudakis, Konstantinos"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@type": "Person", 
      "name": "Christakis, Emmanouil"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@id": "https://orcid.org/0000-0003-3434-3290", 
      "@type": "Person", 
      "name": "Drakoulis, Petros"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@id": "https://orcid.org/0000-0002-4337-1720", 
      "@type": "Person", 
      "name": "Doumanoglou, Alexandros"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@id": "https://orcid.org/0000-0002-7898-9344", 
      "@type": "Person", 
      "name": "Zioulis, Nikolaos"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@type": "Person", 
      "name": "Zarpalas, Dimitrios"
    }, 
    {
      "affiliation": "Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)", 
      "@type": "Person", 
      "name": "Daras, Petros"
    }
  ], 
  "url": "https://zenodo.org/record/3137854", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-11-26", 
  "headline": "Comparing CNNs and JPEG for Real-Time Multi-view Streaming in Tele-Immersive Scenarios", 
  "keywords": [
    "Video", 
    "Compression", 
    "Tele-Immersion", 
    "3D Media Streaming", 
    "Performance Evaluation"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1109/SITIS.2018.00022", 
  "@id": "https://doi.org/10.1109/SITIS.2018.00022", 
  "@type": "ScholarlyArticle", 
  "name": "Comparing CNNs and JPEG for Real-Time Multi-view Streaming in Tele-Immersive Scenarios"
}
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