Efficient Medical Image Compression Based on Integer Wavelet Transform

Krishnaswamy, Ramachandran; Nirmaladevi, S.

doi:10.1109/icbsii49132.2020.9167597

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Over the course of the previous several decades, many proposals for image compression standards have been made by international standardization groups. Medical images compression approaches have already been evaluated using machine learning and IoT [1][2][3][4][5][6][7][8][9][10]. With a focus on more recent standards that were overlooked, this paper covers the present status of image compression standards in medical imaging applications and tackles certain legal and regulatory issues around the use of compression in medical settings using machine learning and IoT framework.…”

Section: Literature On Redundancy Allocationmentioning

confidence: 99%

Machine Learning based Image Compression by Reducing Dimensionality

Sharma,

Srivast,

Kumar

et al. 2024

Preprint

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As medical imaging technologies continue to proliferate and generate massive amounts of data, data compression has become increasingly important for their storage, transit, and processing in the modern world. In telemedicine practices, it is common for medical images to be sent from one site to another. It is now important to minimize image size while maintaining diagnostic information in order to transmit and keep these high-quality images. Medical images compression is a technique that helps save money on transfer and storage by advising on the use of lossy and lossless compression algorithms. Medical images compression calls for a machine learning and IOT infrastructure. Multistage PCA, the Discrete Anamorphic Stretch Transform, and PCA using the Johnson-Lindenstrauss Lemma algorithm are just a few examples of dimensionality reduction techniques used in image compression. We provide many options for decreasing the dimensionality of image data. Among these techniques are PCA using the Johnson-Lindenstrauss Lemma algorithm, Discrete Anamorphic Stretch Transform, and Multistage Principal Component Analysis. The research suggests that combining PCA based on the Johnson-Lindenstrauss lemma with Shannon-Fano coding can lead to significant compression gains.

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Section: Literature On Redundancy Allocationmentioning

confidence: 99%

Machine Learning based Image Compression by Reducing Dimensionality

Sharma,

Srivast,

Kumar

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…AVM (x,y) = θ1Ab(x,y) + θ2As(x,y) + θ3Ae(x,y) (6) Yes Yes Yes [14] Yes Yes Yes [15] Yes [16] Yes Yes [17] Yes [18] Yes Yes [19] Yes Yes [20] Yes Yes Yes Yes Yes [21] Yes Yes [22] Yes [23] Yes Yes…”

Section: Multi-scale Structural Similarity Index (Ms-ssim)mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

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“…The analysis of medical images is reported in [13]. It is shown that the number of medical images is growing rapidly, so effective image compression algorithms are needed to store them.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

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Efficient Medical Image Compression Based on Integer Wavelet Transform

Cited by 7 publications

References 3 publications

Machine Learning based Image Compression by Reducing Dimensionality

Machine Learning based Image Compression by Reducing Dimensionality

Survey On Parameters of Data Compression

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

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