液晶与显示, 2019, 34 (11): 1124, 网络出版: 2019-12-10   

实时视频流缩放系统设计

Design of real-time video stream scaling system
作者单位
1 南京信息工程大学 自动化学院, 江苏 南京 210044
2 江苏省大气环境与装备技术协同创新中心, 江苏 南京 210044
3 南京信息工程大学 大气物理学院, 江苏 南京 210044
摘要
针对目前视频源的分辨率与显示器支持的分辨率不对应, 导致无法在目标显示器上播放的问题, 提出一种实时视频流缩放系统的硬件结构设计。该系统基于双三次插值图像算法, 采用了流水线的设计思路对算法的浮点运算进行优化, 采用乒乓操作的思想避免了视频流输入与输出不同步的情况, 减少了计算延时, 易于FPGA的实现, 在提高系统运算速度的同时减少了对硬件资源的消耗, 并在VIVADO环境中对该设计进行测试验证, 实现了任意比例缩放。实验结果表明, 该图像缩放系统支持分辨率为3 840×2 160的实时图像缩放, 并以最低100×100的分辨率输出, 达到了预期的效果。
Abstract
The resolution of the video source does not correspond to the resolution supported by the display, causing problems that cannot be played on the target display. A hardware architecture design of real-time video stream scaling system is proposed. The system is based on bicubic interpolation image algorithm, the pipeline design idea is adopted to optimize the floating-point operation of the algorithm,and the idea of pingpong operation avoids the asynchronism between input and output of video stream, which reduces the computation delay and eases the implementation of the FPGA. It improves the operation speed of the system and reduces the consumption of hardware resources. The design is tested and verified in the VIVADO environment, and achieves arbitrary scaling. Finally, the design was tested and verified in the VIVADO environment, and any scaling was achieved, which confirmed the correctness of the design. The experimental results show that the image zooming system supports real-time image zooming with a resolution of 3 840×2 160, and outputs the image at a resolution of 100×100, which achieves the desired results.
参考文献

[1] 吴以凯.基于FPGA的视频缩放的设计与实现[D].镇江: 江苏大学, 2017.

    WU Y K.Design and implementation of video scaling based on FPGA [D]. Zhenjiang: Jiangsu University, 2017. (in Chinese)

[2] RETTKOWSKI J, BOUTROS A, GHRINGER D. HW/SW co-design of the HOG algorithm on a Xilinx Zynq SoC [J]. Journal of Parallel and Distributed Computing, 2017, 109: 50-62.

[3] KRYJAK T, KOMORKIEWICZ M, GORGON M. Real-time hardware-software embedded vision system for ITS smart camera implemented inZynq SoC [J]. Journal of Real-Time Image Processing, 2018, 15(1): 123-159.

[4] SENOUCI B, CHARFI I, HEYRMAN B,et al. Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study [J]. Journal of Real-Time Image Processing, 2016, 12(4): 649-662.

[5] SVETEK A, BLAKE M, HERMIDA M C, et al. The calorimeter trigger processor card: the next generation of high speed algorithmic data processing at CMS [J]. Journal of Instrumentation, 2016, 11(2): C02011.

[6] 吴以凯, 喻金华, 肖铁军.基于FPGA的视频缩放设计与实现[J].软件导刊, 2017, 16(8): 83-85.

    WU Y K, YU J H, XIAO T J. Design and implementation of video scaling based on FPGA [J].Software Guide, 2017, 16(8): 83-85. (in Chinese)

[7] 陈志杰, 凌朝东, 魏腾雄.双三次卷积模板插值算法的FPGA实现[J].液晶与显示, 2014, 29(1): 71-76.

    CHEN Z J, LING C D, WEI T X. Design of bicubic convolution template algorithm based on FPGA [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(1): 71-76. (in Chinese)

[8] 王健, 于鸣, 任洪娥.一种用于图像拼接的改进ORB算法[J].液晶与显示, 2018, 33(6): 520-527.

    WANG J, YU M, REN H E. Improved ORB algorithm used in image stitching [J]. Chinese Journal of Liquid Crystals and Displays, 2018, 33(6): 520-527. (in Chinese)

[9] 王平, 全吉成, 赵柏宇.基于双线性插值的图像缩放在GPU上的实现[J].微电子学与计算机, 2016, 33(11): 129-132.

    WANG P, QUAN J C, ZHAO B Y. Realization of image zooming in GPU based on bilinear interpolation [J]. Microelectronics & Computer, 2016, 33(11): 129-132. (in Chinese)

[10] 冉峰, 李天, 季渊, 等.基于FPGA的图像缩放算法设计[J].光电子技术, 2017, 37(1): 21-26.

    RAN F, LI T, JI Y,et al. Design of image scaling algorithm based on FPGA [J]. Optoelectronic Technology, 2017, 37(1): 21-26. (in Chinese)

[11] 岳鑫, 肖晨.基于奇异值分解和双三次插值的图像缩放算法改进[J].西安邮电大学学报, 2018, 23(4): 72-77.

    YUE X, XIAO C. Improvement of imagescaling algorithm based on singular value decomposition and bicubic interpolation [J]. Journal of Xi'an University of Posts and Telecommunications, 2018, 23(4): 72-77. (in Chinese)

[12] 余成波, 李彩虹, 曾亮.K-means指纹定位的优化算法[J].电子技术应用, 2018, 44(2): 70-74.

    YU C B, LI C H, ZENG L.Optimization algorithm of K-means fingerprint location [J]. Application of Electronic Technique, 2018, 44(2): 70-74. (in Chinese)

[13] KOOKARINRAT P, TEMTANAPAT Y. Analysis of range-based key properties for sharded cluster of MongoDB [C]//Proceedings of the 2015 2nd International Conference on Information Science and Security. Seoul, South Korea: IEEE, 2015: 1-4.

[14] KANG Y S, PARK I H, RHEE J,et al. MongoDB-based repository design for IoT-generated RFID/sensor big data [J]. IEEE Sensors Journal, 2016, 16(2): 485-497.

[15] CAI W J, XU Z H, LI Z Q. A high performance surf image feature detecting system based on ZYNQ [C]//Proceedings of the 2017 2nd International Conference on Computer Engineering, Information Science and Internet Technology. 2017: 101-110.

[16] XILINX C.Xilinx Corporation 7 Series Devices Memory Interface Solutions (UG586) [M]. Philadelphia, USA: Xilinx Corporation, 2013.

[17] 刘珂.基于ZYNQ的高速图像采集处理平台设计与验证[D].济南: 山东大学, 2016.

    LIU K.Design and verification of high speed image gathering and processing platform based on ZYNQ [D]. Ji’nan: Shandong University, 2016. (in Chinese)

严飞, 陆宝毅, 刘银萍, 刘卿卿, 陈伟. 实时视频流缩放系统设计[J]. 液晶与显示, 2019, 34(11): 1124. YAN Fei, LU Bao-yi, LIU Yin-ping, LIU Qing-qing, CHEN Wei. Design of real-time video stream scaling system[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(11): 1124.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!