DNN-Based Overhead Reduction for High-Quality Soft Delivery
| Autor: | Toshiaki Koike-Akino, Takashi Watanabe, Philip Orlik, Takuya Fujihashi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Analog transmission
Computer science Image quality 020206 networking & telecommunications Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Iterative reconstruction Reduction (complexity) Metadata Transmission (telecommunications) Computer engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Encoder Decoding methods |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/globecom38437.2019.9014124 |
| Popis: | Soft delivery, i.e., analog transmission, has been proposed to provide graceful video/image quality even in unstable wireless channels. However, existing analog schemes require a significant amount of metadata for power allocation and decoding operations. It causes large overheads and quality degradation due to rate and power losses. Although the amount of overheads can be reduced by introducing Gaussian Markov random field (GMRF) model, the model mismatch can degrade reconstruction quality. In this paper, we propose a novel analog transmission scheme to simultaneously reduce the overheads and yield better reconstruction quality. The proposed scheme uses a deep neural network (DNN) for metadata compression and decompression. Specifically, the metadata is compressed into few variables using the proposed DNN-based metadata encoder before transmission. The variables are then transmitted and decompressed at the receiver for high-quality video/image reconstruction. Evaluations using test images demonstrate that our proposed scheme reduces overheads by 80.0% with 11.2 dB improvement of reconstruction quality compared to the existing analog transmission schemes. |
| Databáze: | OpenAIRE |
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