星地协同的卫星视频高效压缩方法
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摘要
新兴的视频卫星数据采集速率与星地实时传输速率间存在巨大差异。传统视频编码技术通过去除单个视频中短时局部冗余以降低待传输数据量,但仍无法满足在现有星地传输带宽下实时数据传输的需求。长程背景冗余由多个遥感视频对同一观测区域重复拍摄所引发,广泛存在于具有一定时间跨度的重复拍摄的卫星遥感视频数据中。针对这一冗余去除问题,首先探讨了影响地貌背景变化的主要因素,提出了长程背景参考字典;其次,提出了基于长程背景参考字典的参考帧生成方法;最后,提出了基于长程背景参考的卫星视频编解码框架。实验表明,通过引入长程背景字典参考,卫星视频压缩后码率较现有方法降低64.38%,有助于推动卫星视频实时监测的发展与应用。
Video satellites have great potential for military and civil use due to their capabilities in the dynamic remote surveillance. However, the video data collection rate is far higher than the bandwidth between satellite and earth. Video coding is the technology to compression the video data to a low bitrate for transmission, by eliminating the local spatial/temporal redundancies, but there is still a gap between the bitrate of the video data and the satellite-earth bandwidth. Long-term background redundancy is a new type of redundancy existing in the remote satellite video data, caused by the repeated recording of the same place. This type of redundancy becomes significant as the video data of the same place increases. In this paper, we first discuss the factors causing the change of the background, and propose the long-term background reference library. After that, we propose the method for the background reference generation and the coding framework for satellite video data. Experimental results show that 64.38% reduction on video data bitrate can be achieved by using the proposed method, compared to the H.264 video coding standard. The proposed method will boost the applications of video satellite data in the surveillance field.
Video satellites have great potential for military and civil use due to their capabilities in the dynamic remote surveillance. However, the video data collection rate is far higher than the bandwidth between satellite and earth. Video coding is the technology to compression the video data to a low bitrate for transmission, by eliminating the local spatial/temporal redundancies, but there is still a gap between the bitrate of the video data and the satellite-earth bandwidth. Long-term background redundancy is a new type of redundancy existing in the remote satellite video data, caused by the repeated recording of the same place. This type of redundancy becomes significant as the video data of the same place increases. In this paper, we first discuss the factors causing the change of the background, and propose the long-term background reference library. After that, we propose the method for the background reference generation and the coding framework for satellite video data. Experimental results show that 64.38% reduction on video data bitrate can be achieved by using the proposed method, compared to the H.264 video coding standard. The proposed method will boost the applications of video satellite data in the surveillance field.
引文
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[15] Wang Feng, You Hongjian, Fu Xingyu. Auto-adaptive Well-Distributed Scale-Invariant Feature for SAR Images Registration [J]. Geomatics and Information Science of Wuhan University, 2015, 40(2): 159-163(王峰, 尤红建, 傅兴玉. 应用于SAR图像配准的自适应SIFT特征均匀分布算法[J]. 武汉大学学报·信息科学版, 2015,40(2):159-163)
[16] Xiao Xiongwu, Li Deren, Guo Bingxuan, et al. A Robust and Rapid Viewpoint-Invariant Matching Method for Oblique Images [J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1 151-1 159(肖雄武, 李德仁, 郭丙轩, 等. 一种具有视点不变性的倾斜影像快速匹配方法[J]. 武汉大学学报·信息科学版, 2016,41(9): 1 151-1 159)
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[19] Reinhard E, Ashikhmin M, Gooch B, et al. Color Transfer Between Image[J]. IEEE Computer Graphics & Applications, 2002, 21(5):34-41
[20] Liu Tao. Development of Video Satellite [J]. Space International, 2014, 429: 50-56(刘韬. 国外视频卫星发展研究[J].国际太空, 2014, 429: 50-56)
[21] Bjontegaard G. Calculation of Average PSNR Difference Between RD-curves [C]. Document VCEG-M33, ITU-T VCEG Meeting, Austin, TX, USA,2001
[2] Sullivan G J, Ohm J, Han W,et al. Overview of the High Efficiency Video Coding Standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(12):1 649-1 668
[3] Wiegand T, Sullivan G J, Bjontegaard G,et al. Overview of the H.264/AVC Video Coding Standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(7):560-576
[4] Yin L, Hu R, Chen S, et al. Block-Based Global and Multiple-Reference Scheme for Surveillance Video Coding [C]. Pacific Rim Conference on Multimedia, Gwangju, Korea, 2015
[5] Zhang X,Huang T, Tian Y, et al. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding[J]. IEEE Transactions on Image Processing, 2014, 23(2): 769-784
[6] Zhang X, Tian Y, Huang T, et al. Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos with Background Modeling[J]. IEEE Transactions on Image Processing, 2014, 23(10): 4 511-4 526
[7] Huang Tiejun, Zhang Xianguo, Tian Yonghong, et al. IEEE 1857 Standard for High Efficiency Surveillance Video Compression and Recognition [J]. Electronic Engineering and Products World,2013, 20(7): 22-26(黄铁军, 张贤国, 田永鸿, 等. 支持监控视频高效压缩与识别的IEEE 1857标准[J]. 电子产品世界, 2013, 20(7): 22-26)
[8] Yue H, Sun X, Yang J, et al. Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression[J]. IEEE Transactions on Multimedia, 2013, 15(4): 845-857
[9] Shi Z, Sun X, Wu F. Feature-Based Image Set Compression[C]. IEEE International Conference on Multimedia and Expo,California, USA, 2013
[10] Wu H, Sun X, Yang J, et al. Lossless Compression of JPEG Coded Photo Collections[J]. IEEE Transactions on Image Processing, 2016, 25(6): 2 684-2 696
[11] Wang H, Tian T, Ma M, et al. Joint Compression of Near-Duplicate Videos[J]. IEEE Transactions on Multimedia. 2017, 19(5): 908-920
[12] Ma C, Liu D, Peng X, et al. Surveillance Video Coding with Vehicle Library[C].IEEE International Conference on Image Processing,Beijing, China, 2017
[13] Xiao J, Liao L, Hu J, et al. Exploiting Global Redundancy in Big Surveillance Video Data for Efficient Coding[J]. Cluster Computing, 2015, 18(2): 531-540
[14] Xiao J, Hu R, Liao L, et al. Knowledge-Based Coding of Objects for Multi-source Surveillance Vi-deo Data[J]. IEEE Transactions on Multimedia, 2016, 18(9): 1 691-1 706
[15] Wang Feng, You Hongjian, Fu Xingyu. Auto-adaptive Well-Distributed Scale-Invariant Feature for SAR Images Registration [J]. Geomatics and Information Science of Wuhan University, 2015, 40(2): 159-163(王峰, 尤红建, 傅兴玉. 应用于SAR图像配准的自适应SIFT特征均匀分布算法[J]. 武汉大学学报·信息科学版, 2015,40(2):159-163)
[16] Xiao Xiongwu, Li Deren, Guo Bingxuan, et al. A Robust and Rapid Viewpoint-Invariant Matching Method for Oblique Images [J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1 151-1 159(肖雄武, 李德仁, 郭丙轩, 等. 一种具有视点不变性的倾斜影像快速匹配方法[J]. 武汉大学学报·信息科学版, 2016,41(9): 1 151-1 159)
[17] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110
[18] Sedaghat A, Ebadi H. Distinctive Order Based Self-Similarity Descriptor for Multi-sensor Remote Sen- sing Image Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 62-71
[19] Reinhard E, Ashikhmin M, Gooch B, et al. Color Transfer Between Image[J]. IEEE Computer Graphics & Applications, 2002, 21(5):34-41
[20] Liu Tao. Development of Video Satellite [J]. Space International, 2014, 429: 50-56(刘韬. 国外视频卫星发展研究[J].国际太空, 2014, 429: 50-56)
[21] Bjontegaard G. Calculation of Average PSNR Difference Between RD-curves [C]. Document VCEG-M33, ITU-T VCEG Meeting, Austin, TX, USA,2001