Image compression using self-organization networks

Chen, O.T.-C.; Sheu, B.J.; Fang, Wai-Chi

doi:10.1109/76.322995

Cited by 46 publications

(18 citation statements)

References 19 publications

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“…Particularly, in the past decade numerous attempts have been made to pursue the possibility of using various neural networks (NNs) for image compression (see, for example, [9], [11], and [14] for reviews). Autoassociative neural networks [3], the Kohonen self-organizing map (SOM) [1], [7], cellular neural networks [21], [24], and counter-propagation neural networks [23], among others, have been proposed in the literature. In this paper, we are particularly interested in multilayer perceptron (MLP)-type feedforward NNs (FNNs) due to their structural elegance, abundance of training algorithms, and good generalization capabilities [6], [19].…”

Section: Introductionmentioning

confidence: 99%

Application of adaptive constructive neural networks to image compression

Khorasani

2002

IEEE Trans. Neural Netw.

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The objective of the paper is the application of an adaptive constructive one-hidden-layer feedforward neural networks (OHL-FNNs) to image compression. Comparisons with fixed structure neural networks are performed to demonstrate and illustrate the training and the generalization capabilities of the proposed adaptive constructive networks. The influence of quantization effects as well as comparison with the baseline JPEG scheme are also investigated. It has been demonstrated through several experiments that very promising results are obtained as compared to presently available techniques in the literature.

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Section: Introductionmentioning

confidence: 99%

Application of adaptive constructive neural networks to image compression

Khorasani

2002

IEEE Trans. Neural Netw.

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“…Image compression using the self-organization network has been proposed [5]. The learning vector quantization (LVQ) algorithm [6] successfully generates codebooks.…”

Section: Introductionmentioning

confidence: 99%

<title>Classification of interframe difference image blocks for video compression</title>

Kong

2002

SPIE Proceedings

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This paper presents classification of difference image blocks between the two successive image frames for video data compression. Difference blocks are classified to several activity categories according to the image activity distribution. The classification procedure goes in two steps: activity classification and distribution classification. In the activity classification, each interframe difference image block is classified into active or not-active class according to the amount of motion contained in the block. Distribution classification further classifies active image blocks to four activity categories, vertical, horizontal, diagonal, and uniform activities, based on the activity distribution measured by the edge feature vector in the discrete cosine transform domain. A multiplayer feedforward neural network, trained with a small set of sample classification data, successfully classified difference image blocks according to edge feature distribution. The classification scheme improves the performance of video compression at a cost of small increase in the overhead associated with the quantizer switching.

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“…In order to investigate the applicability of the CNN algorithm to real-world problems, the CNN is applied to image compression problems [14], [15]. When the block size is 3 3, the original image data, LENA (255 255) as shown in Fig.…”

Section: B Image Compression Problemmentioning

confidence: 99%

Centroid neural network for unsupervised competitive learning

Park

2000

IEEE Trans. Neural Netw.

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Abstract-An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date. This paper also explains algorithmic relationships among the CNN and some of the conventional unsupervised competitive learning algorithms including Kohonen's self-organizing map (SOM) and Kosko's differential competitive learning (DCL) algorithm. The CNN algorithm requires neither a predetermined schedule for learning coefficient nor a total number of iterations for clustering. The simulation results on clustering problems and image compression problems show that CNN converges much faster than conventional algorithms with compatible clustering quality while other algorithms may give unstable results depending on the initial values of the learning coefficient and the total number of iterations.

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Image compression using self-organization networks

Cited by 46 publications

References 19 publications

Application of adaptive constructive neural networks to image compression

Application of adaptive constructive neural networks to image compression

<title>Classification of interframe difference image blocks for video compression</title>

Centroid neural network for unsupervised competitive learning

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