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A repartition method improving visual quality for PCA image coding
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DOI：10.1016/j.asoc.2014.12.029.png)
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Image coding using principal component analysis (PCA), a type of image compression technique, projects image blocks to a subspace that can preserve most of the original information. However, the blocks in the image exhibit various inhomogeneous properties, such as smooth region, texture, and edge, which give rise to difficulties in PCA image coding. This paper proposes a repartition clustering method to partition the data into groups, such that individuals of the same group are homogeneous, and vice versa. The PCA method is applied separately for each group. In the clustering method, the genetic algorithm acts as a framework consisting of three phases, including the proposed repartition clustering. Based on this mechanism, the proposed method can effectively increase image quality and provide an enhanced visual effect. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Principal component analysis
K-means clustering
Genetic algorithm
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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