This paper describes techniques for classifying video frames using statistical models of reduced DCT or Hadamard transform coefficients. When decimated in time and reduced using truncation or principal component analysis, transform coefficients taken across an entire frame image allow rapid modeling, segmentation, and similarity calculation. Unlike color-histogram metrics, this approach models image composition and works on grayscale images. Modeling the statistics of the transformed video frame images gives a likelihood measure that allows video to be segmented, classified, and ranked by similarity for retrieval. Experiments are presented that show an 87% correct classification rate for different classes. Applications are presented including a content-aware video browser.