Image compression via optimal vector quantization: a comparison between SOM, LBG and k-means algorithms

Corral, Javier; Guerrero, María José Abásolo; Zufiria, Pedro J.

doi:10.1109/icnn.1994.374873

Cited by 8 publications

(1 citation statement)

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“…A variety of modified k-means algorithms have been applied to image compression and image segmentation [1], [2], and [3]. However, these schemes do not consider data as correlated slices, but they are only still image coding techniques applied on one by one slices.…”

Section: Introductionmentioning

confidence: 99%

<title>Adaptive vector quantization of MR images using online k-means algorithm</title>

Shademan

Zia

2001

SPIE Proceedings

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The k-means algorithm is widely used to design image codecs using vector quantization (VQ). In this paper, we focus on an adaptive approach to implement a VQ technique using the online version of k-means algorithm, in which the size of the codebook is adapted continuously to the statistical behavior of the image. Based on the statistical analysis of the feature space, a set of thresholds are designed such that those codewords corresponding to the low-density clusters would be removed from the codebook and hence, resulting in a higher bit-rate efficiency. Applications of this approach would be in telemedicine, where sequences of highly correlated medical images, e.g. consecutive brain slices, are transmitted over a low bit-rate channel. We have applied this algorithm on magnetic resonance (MR) images and the simulation results on a sample sequence are given. The proposed method has been compared to the standard k-means algorithm in terms of PSNR, MSE, and elapsed time to complete the algorithm.

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Section: Introductionmentioning

confidence: 99%