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

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。

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.