1/5/2024 0 Comments Vector light compressorMishra, S., Bhende, C.N.: Bacterial foraging technique-based optimized active power filter for load compensation. In: Proceedings of the 9th International Conference on Parallel Problem Solving from Nature, pp. Tripathy, M., Mishra, S., Lai, L.L., Zhang, Q.P.: Transmission loss reduction based on FACTS and bacteria foraging algorithm. Mishra, S.: A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. In: ICSS2005 International Conference on System& Signals, pp. Pasi, F.: Genetic algorithm with deterministic crossover for vector quantization. Tsekouras, G.E., Mamalis, A., Anagnostopoulos, C., Gavalas, D., Economou, D.: Improved batch fuzzy learning vector quantization for image compression. Tsao, E.C.K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen clustering networks. 40(2), 310–322 (1992)Ĭhan, C.K., Ma, C.K.: A fast method for designing better codebooks for image quantization. Zeger, K., Vaisey, J., Gersho, A.: Globally optimal vector quantizer design by stochastic relaxation. Pal, N.R., Bezdek, J.C., Hathaway, R.J.: Sequential competitive learning and the fuzzy c-means clustering algorithms. Karayiannis, N.B., Pai, P.I.: Fuzzy vector quantization algorithms and their application in image compression. Karayiannis, N.B.: A methodology for constructing fuzzy algorithms for learning vector quantization. 23, 421–427 (1968)įeng, H.M., Chen, C.Y., Ye, F.: Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Zadeh, L.A.: Probability measures of fuzzy events. Shen, F., Hasegawa, O.: An adaptive incremental LBG for vector quantization. Linde, Y., Buzzo, A., Gray, R.M.: An algorithm for vector quantization design. Gonzalez, A.I., Grana, M., Cabello, J.R., Anjou, A.D., Albizouri, F.X.: Experimental results of an evolution Based strategy for VQ image filtering. Pedreira, C.E.: Learning vector quantization with training data selections. Tsekouras, G.E.: A fuzzy vector quantization approach to image compression. Oehler, K.L., Gray, R.M.: Combining image compression and classification using vector quantization. Pearson Education India, Fifth Indian Reprint (2000) Gonzalez, R.C., Woods, R.E.: Digital image Processing. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. The usefulness of the proposed adaptive BFO algorithm, along with the basic BFO algorithm, has been demonstrated by implementing them for a number of benchmark images, and their performances have been compared with other contemporary methods, used to solve similar problems. The codebook design procedure has been implemented using a fuzzy membership-based method, and the optimization procedure attempts to determine suitable free parameters of these fuzzy sets. An improved methodology is proposed here, over the basic BFO scheme, to perform the chemotaxis procedure within the BFO algorithm in a more efficient manner, which is utilized to solve this image compression problem. This chapter demonstrates how such efficient VQ schemes can be developed where the near optimal codebooks can be designed by employing a contemporary stochastic optimization technique, namely bacterial foraging optimization (BFO), that mimics the foraging behavior of a common type of bacteria, Escherichia coli, popularly known as E. Generation of a near optimal codebook that can simultaneously achieve a very high compression ratio and yet maintain required quality in the reconstructed image (by achieving a high peak-signal-to-noise-ratio (PSNR)), to provide high fidelity, poses a real research challenge. Vector quantization (VQ) techniques are well-known methodologies that have attracted the attention of research communities all over the world to provide solutions for image compression problems.
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