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基于关键帧和指示符运动模型的教学视频压缩算法

Teaching Video Compression Algorithm Based on Key Frame and Indicator Movement Model

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摘要

为进一步提高教学视频的压缩比及其制作效率, 针对教学视频中有效信息暂留时间持续较长、信息展示区域固定等特点, 提出一种基于关键帧检测和指示符运动建模的智能教学视频压缩算法。首先检测投影区域作为每帧图像的有效区域, 减少知识冗余; 然后通过变化检测确定视频帧的类型, 并对光标和激光笔投影点建立指示符运动模型, 进一步减少教学视频特有的知识冗余; 最后针对关键帧编码及指示符运动模型设计了相应的播放算法用于视频回放。实验结果表明, 针对以幻灯片投影区域为有效信息的教学视频, 与H.264标准相比, 本文算法在相同峰值信噪比下, 可使码率平均降低约88%, 且编解码过程满足实时要求, 无需额外人工剪辑, 可大幅提高在线教学视频的制作与传输效率。

Abstract

To improve teaching video compression ratio and production efficiency, considering that effective information of teaching video normally remains for a long time and locates in fixed area, a teaching video compression algorithm based on key frame detection and indicator movement modeling is proposed. Firstly, the projection area is detected as the effective region of each frame to reduce the knowledge redundancy. Then, the type of each frame is determined using change detection, and the indicator motion model is established for the mouse cursor and the laser point to reduce more knowledge redundancy. Finally, a corresponding play algorithm based on the key frame coding and indicator motion model is designed to replay the videos by use of OpenCV and MFC. The experimental results showed that, for the teaching videos with effective information in the projection area, the proposed method achieved an average of 88% bitrate reduction than that of H.264 under the same peak signal to noise ratio value. Besides, the encoding and decoding efficiency of this algorithm could meet the real-time requirement. Without extra manual editing, the production and transmission efficiency of online courses could be significantly increased.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/lop55.101005

所属栏目:图像处理

基金项目:全国教育科学规划教育部重点课题(DIA150308)

收稿日期:2018-04-24

修改稿日期:2018-05-03

网络出版日期:2018-05-25

作者单位    点击查看

孟春宁:公安海警学院电子技术系, 浙江 宁波 315801
陈梓铭:公安海警学院电子技术系, 浙江 宁波 315801
冯明奎:公安海警学院电子技术系, 浙江 宁波 315801
赵强:公安海警学院电子技术系, 浙江 宁波 315801

联系人作者:孟春宁(mengchunning123@163.com)

【1】Abomhara M, Khalifa O O, Zakaria O, et al. Video compression techniques: an overview[J]. Journal of Applied Sciences, 2010, 10(16): 1834-1840.

【2】Uhrina M, Frnda J, Sevcik L, et al. Impact of H.264/AVC and H.265/HEVC compression standards on the video quality for 4K resolution[J]. Advances in Electrical and Electronic Engineering, 2014, 12(4): 905-908.

【3】Bauer S, Kneip J, Mlasko T, et al. The MPEG-4 multimedia coding standard: algorithms, architectures and applications[J]. Journal of VLSI signal processing systems for signal, image and video technology, 1999, 23(1): 7-26.

【4】Kwon S K, Tamhankar A, Rao K R. Overview of H.264/MPEG-4 part 10[J]. Journal of Visual Communication and Image Representation, 2006, 17(2): 186-216.

【5】Schwarz, Heiko, Wiegand, Thomas. An improved MPEG-4 coder using lagrangian coder control[C]∥Proceedings of 2001 Video Coding Experts Group Conference on New Tools for Video Compression Technology, 2001: 1-8.

【6】Manjanaik N, Parameshachari B D, Hanumanthappa S N, et al. Intra frame coding in advanced video coding standard (H.264) to obtain consistent PSNR and reduce bit rate for diagonal down left mode using Gaussian pulse[J]. IOP Conference Series: Materials Science and Engineering, 2017, 225(1): 012209.

【7】Sharabayko M P, Markov N G. Fast search for intra prediction mode in H.265/HEVC video compression[J]. Key Engineering Materials, 2016, 685: 897-901.

【8】Purcell D D, Hess C P, Durack J C, et al. Recording, editing, archiving, and distributing radiology lectures: a streamlined approach[J]. Radio Graphics, 2007, 27(6): 1839-1844.

【9】Mittal A, Gupta S, Jain S, et al. Content-based adaptive compression of educational videos using phase correlation techniques[J]. Multimedia Systems, 2006, 11(3): 249-259.

【10】Gu J. The study of compression technology of multimedia educational information based on wavelet[D]. Xi′an: Xidian University, 2009: 25-40.
古佳. 基于小波变换的多媒体教学资源压缩编码方法研究[D]. 西安: 西安电子科技大学, 2009: 25-40.

【11】Barnich O, Droogenbroeck M V. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.

【12】Chen H Y, Qie L Z, Yang D D, et al. Visual background extraction algorithm based on superpixel information feedback[J]. Acta Optica Sinica, 2017, 37(7): 0715001.
陈海永, 郄丽忠, 杨德东, 等. 基于超像素信息反馈的视觉背景提取算法[J]. 光学学报, 2017, 37(7): 0715001.

【13】Mo S W, Deng X P, Wang S, et al. Moving object detection algorithm based on improved visual background extractor[J]. Acta Optica Sinica, 2016, 36(6): 0615001.
莫邵文, 邓新蒲, 王帅, 等. 基于改进视觉背景提取的运动目标检测算法[J]. 光学学报, 2016, 36(6): 0615001.

【14】Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2: 246-252.

【15】Wang H L, Wang J Q, Ding H F, et al. Moving target detection based on the improved gaussian mixture model background difference method[C]∥Advanced Materials Research, 2012, 482: 569-574.

【16】Chen Q, Sheng H X, Zhang Z, et al. Moving object detection under infrared light mutation[J]. Laser & Optoelectronics Progress, 2016, 53(11): 111005.
陈强, 盛惠兴, 张卓, 等. 红外光照突变下的运动目标检测[J]. 激光与光电子学进展, 2016, 53(11): 111005.

【17】St-Charles P L, Bilodeau G A. Improving background subtraction using local binary similarity patterns[C]∥IEEE Winter Conference on Applications of Computer Vision (WACV), 2014: 509-515.

【18】St-Charles P L, Bilodeau G A, Bergevin R. SuBSENSE: a universal change detection method with local adaptive sensitivity[J]. IEEE Transactions on Image Processing, 2015, 24: 359-373.

【19】Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]∥Proceedings of the 17th International Conference on Pattern Recognition, 2004, 2: 28-31.

【20】Seshadrinathan K, Soundararajan R, Bovik A C, et al. Study of subjective and objective quality assessment of video[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1427-1441.

引用该论文

Meng Chunning,Chen Ziming,Feng Mingkui,Zhao Qiang. Teaching Video Compression Algorithm Based on Key Frame and Indicator Movement Model[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101005

孟春宁,陈梓铭,冯明奎,赵强. 基于关键帧和指示符运动模型的教学视频压缩算法[J]. 激光与光电子学进展, 2018, 55(10): 101005

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