Eigen Value based K-means Clustering for Image Compression

Somasundaram, K.; Rani, M. Mary Shanthi

doi:10.5120/ijais12-450583

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…Although this method is not on the bleeding-edge of machine learning techniques for image compression, it does show some promise for replacing traditional methods of image compression. For years, the standard K-Means method has been used in image compression but in 2012, a method proposed by Somasundaram and Rani based on Eigen Values showed improvement over the traditional method [4].…”

Section: A Eigen Value Based K-means Clusteringmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Methods for Image Compression

Battu¹,

Nandy²

2020

IJERT

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Cameras are everywhere in today's world, from security cameras to smartphone cameras and webcams. All of these cameras produce an endless stream of images, which often need to be compressed for efficient storage and/or transmission. Image compression has been researched upon for many decades; however, in recent times, advances in machine learning have achieved great success in many computer vision tasks, and are now gradually being used in image compression. In this paper, we compare techniques involving Eigen Value based K-means clustering, GANs, CAEs, and the Gaussian Mixture Model using Bits Per Pixel(bpp) and PSNR(dB) as metrics

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Section: A Eigen Value Based K-means Clusteringmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Methods for Image Compression

Battu¹,

Nandy²

2020

IJERT

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“…Generally, the compression process can be classified into lossless or lossy [2]. In lossless compression, original image is perfectively reconstructed without loss any information.…”

Section: Fig 1 General Process Of Image Compressionmentioning

confidence: 99%

Modified Haar Wavelet based Method for Compressing Medical Images

Chitra¹,

Rani²

2018

IJCA

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Nowadays, many researchers are attracted by image compression techniques because of the minimization of storage and transmission cost over the internet. The main objective of image compression is to reduce the redundant and irrelevant information from the original data. Haar Wavelet Transformation (HWT) is an efficient transform coding among the lossy image transform techniques. This proposed work has two stages of process: Vector Quantization and Thresholding. In the first stage, Haar Wavelet Transformation is implemented which results categorized into two matrices; Approximation Co-efficient (AC) matrix and Detailed Coefficient (DC) matrices. The second stage of proposed method is applied Vector Quantization(VQ) to improve the compression efficiency. This proposed work achieved higher compression ratio without compromising the image quality. The performance of the proposed method is compared using the traditional Haar Wavelet transform based method which has been evaluated using some popular parameters such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index(SSIM), Compression Ratio (CR) and Bit Rate(BR). The computation cost is minimized using proposed method. The comparative analysis of the proposed method has witnessed the achievement of good compression ratio and image quality as well.

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“…Hence many novel methods have been proposed for selecting the initial seeds for K-means Clustering [8][9]. Two new methods have been proposed in [10][11] which select the initial seeds based on proper blend of statistical parameters mean, median and mode.…”

Section: 2k-means Clustering Algorithmmentioning

confidence: 99%