981 research outputs found

    An Efficient Codebook Initialization Approach for LBG Algorithm

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    In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm

    S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization

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    This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57

    A Lévy Flight Based BAT Optimization Algorithm for Block-based Image Compression

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    Many metaheuristics have been adopted to solve the codebook generation problem in image processing. In this paper, the Bat algorithm is combined by the Lévy flight distribution to find out the global optimum codebook. The Lévy flight distribution is combined by the local search procedure. Therefore most of the time the bat concentrate on the local area for specific food while it rarely flies to the different parts of the field for better food opportunities. This process strongly guides the bat on the global minimum way and offers better food, then the bat flies to that direction. Consequently, if a bat is captured by a local minimum point accidentally, the Lévy flight step provides a chance to escape from it easily. Numerical results suggest that the proposed Lévy flight based Bat algorithm is better than the classical ones and provides the global optimum codebook for image compression

    Image compression techniques using vector quantization

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