Evaluation of image compression algorithms

Pandian, R.

doi:10.1109/ut.2015.7108244

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…In DCT, an outsized portion of the energy is compacted into lower recurrence coefficients to quantization, the overwhelming majority of the upper recurrence coefficient become little or zero and have a twisted to be assembled. [8] The LL sub bands are frequently additionally disintegrated for ensuing degree of decay. On the off chance, that the degree of disintegration expands, the higher subtleties are caught more effectively [9].…”

Section: Wavelet Transformsmentioning

confidence: 99%

A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images

Pandian¹,

Lalithakumari²,

AmalaRani³

et al. 2021

Advances in Parallel Computing

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Image compression is the processing of images by using Transform operations and Encoding techniques. Nowadays, there is an essential need of these methods in the medical field. This work is focused on the performance quality assessment of medical images using Image Compression Techniques. Image compression is the process of compression of an image in such a way that it has less space than the original image. It is an organization of compression technique that reduces the size of an image file without affecting its quality to a greater extent. The bio-orthogonal transform is used for decomposing the Lung images. After decomposition, different methods of encoding are performed and finally the proposed compression methods are evaluated for finding optimum algorithm for medical image.

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Section: Wavelet Transformsmentioning

confidence: 99%

A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images

Pandian¹,

Lalithakumari²,

AmalaRani³

et al. 2021

Advances in Parallel Computing

View full text Add to dashboard Cite

show abstract

“…This approach is based on a clip-level (or threshold value) to turn a gray-scale image into a binary one. The trick to this approach is to pi the thread value [9]. The segmentation goal is to reduce or alter an images portrayal into one that is more concrete unit simpler to interpret.…”

Section: Haralic Texture Featuresmentioning

confidence: 99%

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Pandian

Ravikumar

Kumar

2020

IJECE

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The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy.

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“…[7] and compressed image shown in figure 2. (CR=91.81%, PSNR =59.69) (CR= 21.23%, PSNR=40.79) (CR=6.82%, PSNR = 28.24) Figure 2a.…”

mentioning

confidence: 99%

Effects of Compression Algorithms and Identification of Cancer cell using CT Coronel View Lung Image

Pandian¹,

S²

2019

IJEAT

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Modern radiology techniques provide crucial medical information for radiologists to diagnose diseases and determine appropriate treatments. Hence dealing with medical image compression needs to compromise on good perceptual quality (i.e. diagnostically lossless) and high compression rate. The objective also includes finding out an optimum algorithm for medical image compression algorithm. The objective is also focused towards the selection of the developed image compression algorithm, which do not change the characterization behavior of the image.

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Evaluation of image compression algorithms

Cited by 8 publications

References 10 publications

A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images

A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Effects of Compression Algorithms and Identification of Cancer cell using CT Coronel View Lung Image

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