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


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

  • Konstantinos Konstantoudakis, Emmanouil Christakis, Petros Drakoulis, Alexandros Doumanoglou, Nikolaos Zioulis, Dimitrios Zarpalas, and Petros Daras, "Comparing CNNs and JPEG for Real-Time Multi-view Streaming in Tele-Immersive Scenarios", The 14th International Conference on Signal Image Technology & Internet based Systems (SITIS 2018), Las Palmas de Gran Canaria, Spain, 26-29 November 2018.

  • Full document available here.
    Contact Information

    Dr. Petros Daras, Research Director
    6th km Charilaou – Thermi Rd, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras(at)iti(dot)gr