This paper presents a new adaptive search approach to reduce the computational complexity of fractal encoding. A simple but very efficient adaptive necessary condition is introduced to exclude a large number of unqualified domain blocks so as to speed-up fractal image compression. Furthermore, we analyzed an unconventional affine parameter that has better properties than the conventional luminance offset. Specifically, we formulated an optimal bit allocation scheme for the simultaneous quantizations of the usual scaling and the aforementioned unconventional affine parameter. Experiments on standard images showed that our adaptive search method yields superior performance over conventional fractal encoding.
This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.
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