在基于视频内容检索的多媒体系统中，由于需要进行镜头分割和提取关键帧，还需要用静态图象来表示视频内容以及该图象的特性进行分析，因此根据视频序列中相邻画面一般具有相似性和连续性这一镜头分割和关键帧提取的共同理论依据，构造了关键帧提取系统，它能直接提取关键帧，而不用先进行镜头分割，且只需要Ⅰ帧信息及其频域直流分量的信息，即能达到最小程度的解码，在关键帧的判定方面，通过分析当前镜头分割技术的特点及其发展方向，提出了质点等价法和基于宏块互异的方法。

Keyframes are still images, which best represent the content of the video sequence in an abstracted manner, and may be extracted from original compressed data. Keyframes are frequently used to supplement the text of a video log, but there has been little progress in identifying them automatically. The challenge is that the extraction of keyframes needs to be automatic and content based so that they maintain the important content of the video while removing all redundancy. Up to now, more and more video materials are stored and transmitted in the compression data, so it is practical to study a unified approach to extraction of keyframes based on compressed video data. In this paper, we present a system for extraction of keyframes, which is based on different formulae comparing discrete cosine transform(DCT) direct current(DC) coefficients over the I frames in MPEG video stream, and for which only minimal decoding is needed. In theory semantic primitives of the video, such as interesting objects, actions and events should be used. However, because such general semantic analysis is not currently feasible, we explore two methods instead, in which one is from the idea of dot in mass, and the other is based on the number of unequal macro blocks.