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When applying PDE algorithm according to the complexity of the image, the problem is how to get complexity of the image. J.N. Kim and et al. proposed one method, which results in significant reduced computation with the same predicted image quality of the conventional FS [1]. But by localizing the image well, we can get more the reduction of unnecessary computation. In general, image complexity is localized in pieces rather than the whole span of image or some fixed blocks. If we use localization with small subblocks, the PDE algorithm can be represented as shown in Eq. (1). Generally, image complexity is distributed sparsely in small patches rather than globally in whole image. From the viewpoint, we can well classify image complexity by small subblock unit rather than by whole row vector or column one of matching blocks. It is very important in Eq. (1) to detect the image area with larger matching error. That is, by first checking more complex area of matching block, we can get larger matching error with the same computations compared with the conventional top-to-bottom matching scan. Then we get higher probability to kick off the candidates of impossible motion vector and faster kickoff results in more reduction of computational load in calculating matching error. In Eq. (1), s means the size of small sub-block and local_complexity_order[] matching order according to local complexity of image.

Lossless Fast Full Search Algorithm in Motion Estimation Using Various Matching Scans from Image Localization