针对窄带网络的视频信号传输问题，分析了传统视频代码转换帧速率转换时，由于运动矢量非最佳化所造成的图象质量下降的原因，并提出了一种基于量化误差的自自动化运动矢量模型，从而减小了搜索域，使最佳化输出运行矢量能进行快速运动估值；同时根据灰度系统理论，提出了一种有效的灰度预测搜索方法，另外，又根据DCT系数理论模型。采用自适应快速视频编码方法，进一步提高了编码速度，实验结果表明：该方法不仅改善了视频图象质量，而且计算复杂度也大大减小。

To the video signal transmission on narrow band networks, this paper analyzes the quality degradation that cause the non optimized motion vectors during the traditional video transcoding when the frame rate conversion. We proposed a model of adaptive motion vectors based on the sum of the differential quantization errors, thus reduced search area. It is possible to achieve fast motion estimation for near optimal outgoing motion vectors. Based on the gray system theory, an efficient gray prediction search(GPS) algorithm for block motion estimation is proposed. The GPS can determine the motion vectors of image blocks quickly and correctly. We speed up the encoding in further using a method of adaptive fast video encoding based on the theoretical model for discrete cosine transform(DCT) coefficients. We also present a fast DCT algorithm to speed up the calculation of DCT further when the quantization step size is large. The experimental result show that the proposed scheme improves the video quality, with a minimal increase in computational complexity.