Conference paper Open Access

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

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <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: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: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>
Views 113
Downloads 292
Data volume 371.5 MB
Unique views 99
Unique downloads 271


Cite as