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This paper describes a new architecture for discrete cosine transform (DCT)-based motion estimation and compensation. Previous methods do not take sufficient advantage of the sparseness of two-dimensional (2-D) DCT coefficients to reduce execution time. We first derive a recursion equation for transform domain motion estimation; we then use it to develop a wavefront array processor consisting of highly regular, parallel, and pipelined processing elements that more efficiently performs motion estimation. In addition, we show that the recursion equation enables motion predicted images with different frequency bands, for example, from the images with low-frequency components to the images with low- and high-frequency components. The wavefront array processor can reconfigure to different motion estimation algorithms, such as logarithmic search and three step search, without architectural modifications. These properties can be effectively used to reduce the energy required for video encoding and decoding. Simulation results on video sequences of different characteristics show that the proposed architecture achieves a significant reduction in computational complexity and processing time, with comparable performance to spatial domain approaches with respect to the peak signal to noise ratio (PSNR) and the compression ratio.