DCSN-Cast: Deep compressed sensing network for wireless video multicast

Autor: Suyue Li, Peihao Li, Anhong Wang, Jie Liang, Hehe Wu
Rok vydání: 2019
Předmět:
Zdroj: Signal Processing: Image Communication. 76:56-67
ISSN: 0923-5965
DOI: 10.1016/j.image.2019.04.017
Popis: The purpose of wireless video multicast is to send a video signal simultaneously to multiple heterogeneous users, each of whom desires video quality that matches its channel condition. The current compressed sensing (CS)-based wireless video multicast has some advantages but also some shortcomings, such as high computational and low reconstruction quality, especially at high packet loss rates. This paper aims to improve the current CS-based multicasts, and proposes two deep compressed sensing networks for wireless video multicast, abbreviated as DCSN-Cast. We first consider a residual DCSN-Cast (DCSRN-Cast), which consists of two parts: a fully connected network and a deep residual network. The fully connected network takes the measurements of CS as input and outputs the preliminary reconstructed image, while the deep residual network takes this preliminary reconstructed image and outputs the final reconstructed image. The second scheme is a fully connected deep neural network (DCSFCN-Cast) which prunes the convolutional neural network under consideration to reduce the complexity. Extensive experiments show that the proposed DCSN-Cast outperforms the state-of-the-art CS-based wireless video multicast methods, especially at high packet loss rates.
Databáze: OpenAIRE
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