"The work performed during the ENVISION project aimed at developing a series of advanced methods and algorithms for efficient representation, storage and delivery of visual content pertaining to emerging visual application in the context of the IoT paradigm.
We studied the problem of cache-aided delivery of interactive multiview video content to wireless users and developed a greedy joint caching and scheduling policy that takes into account the way users navigate through the scene and the video delivery constraints [J2, C4]. The performance of this greedy policy is within certain bounds with respect to the optimal solution. We also studied the problem of caching 360-degree video, which arises in the context of virtual reality and augmented reality applications [J3, C2]. To maximize the use of caching resources and eventually ensure the best possible QoE for the users, the proposed caching algorithm takes advantage of 360-degree video encoding into multiple tiles and quality layers to make fine-grained decisions regarding which content to cache in each base station and where to deliver the content from while exploiting the collaboration between the base stations.
Next, we developed a joint source-channel coding (JSCC) algorithm for wireless transmission of images captured by conventional active pixel sensing cameras [J4, C3]. We proposed a radically novel approach based on the autoencoder framework and convolutional neural networks. We showed that through the use of deep models and the backpropagation algorithm, compact source representations can be learned which are also robust to variations in the channel quality.
Finally, we investigated the problem of representation learning for neuromorphic vision sensors (NVS) data [J1, C1]. We developed a novel framework which comprises a compact graph representation for NVS data combined with a spatio-temporal feature learning module based on residual graph-convolutional neural networks. This framework allows for efficient end-to-end learning of features directly from NVS data.
Overall, the work conducted during the ENVISION project resulted in 4 peer-reviewed papers presented at leading international conferences/workshops, and 2 journal peer-reviewed publications in high-impact factor IEEE Trans. Two more journal papers are under review.
[J1] Yin Bi, A. Chadha, A. Abbas, E. Bourtsoulatze and Y. Andreopoulos, ""Graph-based Spatio-Temporal Feature Learning for Neuromorphic Vision Sensing,"" submitted, Nov. 2019
[J2] E. Bourtsoulatze and D. Gunduz, ""Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks,"" submitted, Oct. 2019
[J3] P. Maniotis, E. Bourtsoulatze, and N. Thomos, ""Tile-Based Joint Caching and Delivery of 360o Videos in Heterogeneous Networks,"" IEEE Trans. on Multimedia, Dec. 2019
[J4] E. Bourtsoulatze, D. Burth Kurka, and D. Gunduz, ""Deep Joint Source-Channel Coding for Wireless Image Transmission,"" IEEE Trans. on Cognitive Comms. and Netw., Sept. 2019
[C1] Y. Bi, A. Chadha, A. Abbas, E. Bourtsoulatze and Y. Andreopoulos, ""Graph-Based Object Classification for Neuromorphic Vision Sensing,"" in Proc. of IEEE ICCV, Oct. 2019
[C2] P. Maniotis, E. Bourtsoulatze and N. Thomos, ""Tile-Based Joint Caching and Delivery of 360o Videos in Heterogeneous Networks,"" in Proc. of IEEE MMSP, Sept. 2019
[C3] E. Bourtsoulatze, D. Burth Kurka and D. Gunduz, ""Deep Joint Source-Channel Coding for Wireless Image Transmission,"" in Proc. of ICASSP, May 2019
[C4] E. Bourtsoulatze and D. Gunduz, ""Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks,"" in Proc. of PIMRC, Sept. 2018"
