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Investigating student knowledge transfer and metacognitive activities at college CS departments and at coding bootcamps.

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An improved three-step search (ITSS) block-matching algorithm for motion estimation is described in thisThe approach adopted in block-matching algorithms is first to divide each frame into blocks, typically 16x16 pixels. A motion vector is then calculated for each block in the current frame by searching for the best matching block within a limited search area in the reference/previous frame. Compression is achieved by using this best-matched block, indicated by the motion vector, as the predictor for the current block.Of the block-matching techniques reported in the literature, the full search (FS) method provides the optimal solution by exhaustively evaluating all the possible candidate blocks within the search range in the reference frame. However, massive computation is required in the implementation of FS. In order to speed up the process by reducing the number of search locations, many fast algorithms have been developed, such as the existing three-step search (TSS) algorithm [5] and the recently proposed new three-step search (NTSS) algorithm [6]. Recent studies show that the motion-vector distribution of a real-world image sequence, within the search window, is highly centrebiased. Based on this fact, we propose an improved