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2010•Unpublished venueRequires access

Clonal Selection Algorithm for Image Compression

Ruochen Liu, Licheng Jiao, Wei Zhang, Jingjing Ma

Open publisher page 3 citations

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.

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What this paper is about

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.

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Available 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.

Key concepts: Codebook, Vector quantization, Linde–Buzo–Gray algorithm, Data compression, Image compression, Computer science, Algorithm, Selection (genetic algorithm)

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