| Summary: | 博士 === 義守大學 === 資訊工程學系 === 103 === 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 thesis proposes some clustering methods as follows 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.
Firstly, we apply PCA for image compression. In the PCA computation, we adopt the neural network architecture in which the synaptic weights, served as the principal components, are trained through generalized Hebbian algorithm (GHA). The number of principal components are determined by a pre-specified value such as the non-adaptive procedure. Moreover, we partition the training set into clusters using K-means method in order to obtain better retrieved image qualities. In addition, we replace K-means method with the subtractive clustering method to implement the above procedure.
Secondly, in consideration of image features, we partition full image blocks into four clusters including smooth regions, vertical and horizontal edges, diagonal and subdiagonal edges. Because of the homogeneity, principal component analysis is used to reduce the redundancy of storages inside each cluster through the projection of data based on the principal components. Genetic algorithm is employed to determine the optimal number of components that preserve most of the information of the original data. Basing on this mechanism, we develop an iterative clustering method. The proposed method effectively removes the redundancy and increases the number of principal components in a number of clusters to improve the reconstructed effect of certain clusters with complex structures. It is necessary to define a measurement for the number of recorded variables. Consequently, the retrieved image has high quality and good visual effect than Traditional PCA, the K-means clustering.
Finally, we propose another 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 GA operation, the proposed repartition clustering, and PCA image coding. Based on this mechanism, the proposed method can effectively increase image quality and provide an enhanced visual effect.
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