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This paper proposes a new method of calculating a matching metric for motion estimation. The proposed method splits the information in the source images into multiple scale and orientation subbands, reduces the subband values to a binary representation via an adaptive thresholding algorithm, and uses mutual information to model the similarity of corresponding square windows in each image. A moving window strategy is applied to recover a dense estimated motion field whose properties are explored. The proposed matching metric is a sum of mutual information scores across space, scale, and orientation. This facilitates the exploitation of information diversity in the source images. Experimental comparisons are performed amongst several related approaches, revealing that the proposed matching metric is better able to exploit information diversity, generating more accurate motion fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of “motion hints” to produce interframe predictions. A motion hint is a loose and global description of motion that can be communicated using metadata; it describes a continuous and invertible motion model over multiple frames, spatially overlapping other motion hints. A motion hint provides a reasonably accurate description of motion but only a loose description of where it is applicable; it is the task of the client to identify the exact locations where this motion model is applicable. The focus of this paper is a probabilistic multiscale approach to identifying these locations of applicability; the method is robust to noise, quantization, and contrast changes. The proposed approach employs the Laplacian pyramid; it generates motion hint probabilities from observations at each scale of the pyramid. These probabilities are then combined across the scales of the pyramid starting from the coarsest scale. The computational cost of the approach is reasonable, and only the neighborhood of a pixel is employed to determine a motion hint probability, which makes parallel implementation feasible. This paper also elaborates on how motion hint probabilities are exploited in generating interframe predictions. The scheme of this paper is applicable to closed-loop prediction, but it is more useful in open-loop prediction scenarios, such as using prediction in conjunction with remote browsing of surveillance footage, communicated by a JPEG2000 Interactive Protocol (JPIP) server. We show that the interframe predictions obtained using the proposed approach are good both visually and in terms of PSNR. [ABSTRACT FROM AUTHOR]