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Found 7 papers, 2 papers with code
ECRF: Entropy-Constrained Neural Radiance Fields Compression with Frequency Domain Optimization
Furthermore, to achieve a higher level of sparsity, we propose using an entropy parameterization technique for the frequency domain, specifically for DCT coefficients of the feature-grid.
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72. 4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario.
Open GOP Resolution Switching in HTTP Adaptive Streaming with VVC
The user experience in adaptive HTTP streaming relies on offering bitrate ladders with suitable operation points for all users and typically involves multiple resolutions.
Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things
Furthermore, we demonstrate that the proposed protocol clearly outperforms the classical proactive HARQ in all scenarios when taking a processing delay reduction due to the less complex prediction approach into account, achieving an energy efficiency gain in the range of 11% up to 15% for very stringent latency budgets of 1 ms at $10^{-2}$ BLER and from 4% up to 7. 5% for less stringent latency budgets of 2 ms at $10^{-3}$ BLER.
HTML5 MSE Playback of MPEG 360 VR Tiled Streaming
In this demo paper we describe how we implemented the most advanced media profile from OMAF: HEVC-based viewport-dependent OMAF video profile with multi-resolution HEVC-tiles, using only JavaScript.
Multimedia
Multi-Kernel Prediction Networks for Denoising of Burst Images
The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency.
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC).
