Conference Paper

Clonal Selection Algorithm for Image Compression.

DOI: 10.1109/CEC.2010.5586256 Conference: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010
Source: DBLP

ABSTRACT Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, Clonal Selection Algorithm for Image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

0 Bookmarks
 · 
63 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Central to vector quantization is the design of optimal code book. The construction of a globally optimal code book has been shown to be NP-complete. However, if the partition halfplanes are restricted to be orthogonal to the principal direction of the training vectors, then the globally optimal K-partition of a set of N D-dimensional data points can be computed in O((N+KM/sup 2/)D) time by dynamic programming, where M is the intensity resolution. This constrained optimization strategy improves the performance of vector quantizer over the classic LBG algorithm and the popular methods of tree-structured recursive greedy bipartition of the training data set.< >
    Data Compression Conference, 1992. DCC '92.; 04/1992
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.
    Neural Computing and Applications 01/2005; 14(3):203-211. · 1.76 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The Kohonen's self-organizing map algorithm for vector quantization of images is modified to reduce the edge degradation in the coded image. The learning procedure is performed by adaptive learning rates that are determined according to the image block activity. The simulation result of 4×4 vector quantization for 512×512 image coding demonstrates the feasibility of the proposed method
    IEEE Transactions on Neural Networks 02/1995; · 2.95 Impact Factor