篇名 |
Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission
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並列篇名 | Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission |
作者 | Heri Prasetyo、Alim Wicaksono Hari Prayuda、Chih-Hsien Hsia、Muhammad Alif Wisnu |
英文摘要 | The image companding is a simple image compression technique which is very easy to be implemented in the bandwidth-limited environment. This paper presents a simple way for improving the quality of decompressed image in the image companding task. The proposed method consists of two networks, namely Sub-band Network (SubNet) and Pixel Network (PixNet), for performing an image reconstruction. The SubNet module exploits the effectiveness of Stationary Wavelet Transform (SWT) and Convolutional Neural Network (CNN) in order to recover the lost information in the wavelet sub-bands basis. Whilst, the PixNet part applies CNN with identity mapping to improve the quality of initial reconstructed image obtained from the SubNet module. As reported in this paper, the proposed method outperforms the former existing schemes in the image companding task. It has also been proven that the proposed method is able to improve the quality of reconstructed image with some simple steps.
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起訖頁 | 467-473 |
關鍵詞 | Convolutional neural network、Deep learning、Image companding、Residual networks、Stationary wavelet transform |
刊名 | 網際網路技術學刊 |
期數 | 202205 (23:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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