Through the research of the existing image compression algorithms based on wavelet transformation, and from the construction of wavelet filter, by calculating the image local measures with directional characteristics, this paper selects appropriate measure predictor to separately deal with the lowest frequency sub-band coefficients, at the same time, while more efficiently scanning and positioning, quantitatively coding for the other high frequency sub-band coefficients through means of the maximum table. Experimental results show that the improved algorithm greatly reduces the coding time in the premise of no reconstructed image quality changes.
For the problem of low efficiency in SIFT algorithm while using exhaustive method to search the nearest neighbor and next nearest neighbor of feature points, this paper introduces K-D tree algorithm, to index the feature points extracted in database images according to the tree structure, at the same time, using the concept of a weighted priority, further improves the algorithm, to further enhance the efficiency of feature matching.
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