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摘要:
为了实现有效与可靠的视频传输，针对水下单光子通信系统，提出了一种基于字典学习和LT码级联LDPC码的视频联合编码方案。通过字典学习稀疏编码，极大地压缩了视频数据量。根据水下单光子信道存在的删除特性，利用LT码级联LDPC码的信道级联编码方法，同时克服了LT码译码开销过大的缺点。由于LT码存在译码失败概率的问题，提出了译码成功双反馈机制。实验结果表明，当信道误码率处于10-2数量级、视频压缩率为75.6%时，可以实现平均峰值信噪比(PSNR)为37.4921 dB重建视频帧。
Abstract:In order to achieve effective and reliable video transmission, a video joint coding scheme based on dictionary learning and the concatenation of LT code and LDPC code is proposed for underwater single-photon communication system. Sparse coding based on dictionary learning greatly compresses the amount of video data. According to the deletion characteristic of underwater single-photon channel, using the LT-LDPC channel concatenated coding method can overcome the disadvantage of excessive decoding overhead of LT code. Aiming at the problem of decoding failure probability of LT coding, a double feedback mechanism for decoding success is proposed. The experimental results show that when the channel error rate is in the order of 10-2 and the video compression rate is 75.6%, the video frames can be reconstructed with an average peak signal-to-noise ratio (PSNR) of 37.4921 dB.
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Overview: In recent years, underwater wireless optical communication has played an important role in environmental inspection, marine exploration fields, etc., and has received more and more attention. It has the advantages of high bandwidth, fast speed, strong anti-electromagnetic interference ability, etc. Because the video can convey information intuitively, the transmission of video in underwater wireless optical communication has become a research hotspot. In order to achieve effective and reliable video transmission, a video joint coding scheme based on dictionary learning and concatenation of LT code and LDPC code is proposed for underwater single-photon communication system. The scheme improves the data compression rate and data coding efficiency, and achieves the optimal synchronization of transmission quality and transmission efficiency. Aiming at the problem of large amount of video data, the dictionary learning sparse coding method is used, and each frame of video image transmits the sparse matrix information, and the decoding of each video frames does not affect each other. This method greatly reduces the amount of video data and improves the effectiveness of the communication system. In view of the deletion characteristic of underwater single-photon channel, LT-LDPC channel concatenated coding method is used. LT digital fountain code is specially designed to deal with various deletion channels. As long as enough fountain packets are received, the data can be restored. However, since the decoding overhead is too large, erroneous symbols are corrected by cascading LDPC codes to reduce the decoding overhead. Through experiments, the influence of encoding method and decoding success judgment threshold on decoding overhead is analyzed. The results prove that cascaded coding can overcome the disadvantage of excessive decoding overhead of LT code. At the same time, the decoding overhead decreases with the increase of the judgment threshold of decoding success. Aim to solve the problem of decoding failure probability of LT code, a double feedback mechanism for decoding success is proposed, which can also realize the clear recovery of video frames in a communication environment with a high bit error rate. Experimental results show that as the judgment threshold of decoding success increases, the average PSNR of video frames gradually decreases. As the video compression rate increases, the average PSNR value becomes smaller. When the channel error rate is in the order of 10-2 and the video compression rate is 75.6%, the video frames can be reconstructed with an average peak signal-to-noise ratio (PSNR) of 37.4921 dB.
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图 1 水下单光子视频通信实验系统方案
Figure 1. Experimental system scheme of underwater single-photon video communication
图 2 水下单光子视频通信系统图
Figure 2. Diagram of underwater single-photon video communication system
图 3 基于字典学习和LT码级联LDPC码的视频联合编码方案
Figure 3. Video joint coding scheme based on dictionary learning and the concatenation of LT code and LDPC code
图 5 LT码级联LDPC码的码字设计和实现过程
Figure 5. Codeword design and implementation process of the concatenation of LT code and LDPC cod
图 7 译码成功判定阈值与译码开销关系
Figure 7. The relationship between the judgement thresholds of decoding success and the encoding consumption
图 8 译码成功判定阈值与平均PSNR关系
Figure 8. The relationship between the judgement thresholds of decoding success and the average PSNR
图 9 不同处理方式下的四帧图像比较
Figure 9. Comparison of four frames of images under different processing methods
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