Codebook generation for vector quantisation

Chen, C.-Q.; Koh, Soo Ngee; Sivaprakasapillai, P.

doi:10.1049/el:19950366

Cited by 3 publications

(1 citation statement)

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“…Applications of vector quantization (VQ) in coding images can be seen in many papers [13]- [16]. Recently, efficient image coding techniques have become more and more important in multimedia video transmission [17].…”

Section: B Image Codingmentioning

confidence: 99%

Online learning vector quantization: a harmonic competition approach based on conservation network

Wang

Sun

1999

IEEE Trans. Syst., Man, Cybern. B

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Abstract-This paper presents a self-creating neural network in which a conservation principle is incorporated with the competitive learning algorithm to harmonize equi-probable and equi-distortion criteria [1]. Each node is associated with a measure of vitality which is updated after each input presentation. The total amount of vitality in the network at any time is 1, hence the name conservation. Competitive learning based on a vitality conservation principle is near-optimum, in the sense that problem of trapping in a local minimum is alleviated by adding perturbations to the learning rate during node generation processes. Combined with a procedure that redistributes the learning rate variables after generation and removal of nodes, the competitive conservation strategy provides a novel approach to the problem of harmonizing equi-error and equiprobable criteria. The training process is smooth and incremental, it not only achieves the biologically plausible learning property, but also facilitates systematic derivations for training parameters. Comparison studies on learning vector quantization involving stationary and nonstationary, structured and nonstructured inputs demonstrate that the proposed network outperforms other competitive networks in terms of quantization error, learning speed, and codeword search efficiency.

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Section: B Image Codingmentioning

confidence: 99%