Abstract
This chapter shows how neural fuzzy systems can improve motion estimation and compensation for video compression. Motion estimation and compensation are key parts of video compression. They help remove temporal redundancies in images. Most motion estimation algorithms neglect the strong-temporal correlations within the motion field. The search windows stay the same through the image sequences and the estimation needs heavy computation. We use an unsupervised neural vector quantizer system can use the temporal correlation of the motion field to estimate the motion vectors. First-order and second-order statistics of the motion vectors give ellipsoidal search windows. This algorithm reduces the search area and entropy and gives clustered motion fields. Motion-compensated video coding further assumes that each block of pixels moves with uniform translational motion. This often does not hold and can produce block artifacts. We use a supervised neural fuzzy system to compensate for the overlapped block motion. This fuzzy system uses the motion vectors of neighboring blocks to map the prior frame’s pixel values to the current pixel value. The neural fuzzy system used 196 rules that came from the prior decoded frame. The fuzzy system learns and updates its rules as it decodes the image. The fuzzy system also improved the compensation accuracy. The appendix derives both the fuzzy system and the supervised neural-like learning laws that tune its parameters.
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Kim, H.M., Kosko, B. (2000). Motion Estimation and Compensation with Neural Fuzzy Systems. In: Pal, S.K., Ghosh, A., Kundu, M.K. (eds) Soft Computing for Image Processing. Studies in Fuzziness and Soft Computing, vol 42. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1858-1_21
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DOI: https://doi.org/10.1007/978-3-7908-1858-1_21
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