Design and Implementation for JPEG-LS Algorithm Based on FPGA

Shang, Yuanyuan; Niu, Huizhuo; Ma, Shugen; Hou, Xuefeng; Chen, Chuan

doi:10.1007/978-3-642-24091-1_48

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…With the improvement of medical imaging facilities, a growing amount of data is brought out in the modern image processing, and it leads to an increasingly heavy burden for data storage and transmission [1]. Image compression is a technique of reducing the redundancies in image and represents it in shorter manner [2], which can allow more cost-effective utilization of network bandwidth and storage capacity.…”

Section: Introductionmentioning

confidence: 99%

An improved medical image compression technique with lossless region of interest

Zuo

Lan

Deng

et al. 2015

Optik

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Section: Introductionmentioning

confidence: 99%

An improved medical image compression technique with lossless region of interest

Zuo

Lan

Deng

et al. 2015

Optik

View full text Add to dashboard Cite

“…With the rapidly growing use of medical images in diagnosis procedures, the storage space and bandwidth required to store and communicate these images are exponentially increasing [1]. Accordingly, significant emphasis has been attracted by image compression techniques, according to their ability to reduce the size of the data required to represent the image, by eliminating redundant information [2].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

Khaleel

Zahri

2021

Journal of Engineering

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The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.

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Face recognition in JPEG compressed domain: a novel coefficient selection approach

Moin

Sepas-Moghaddam

2013

SIViP

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Design and Implementation for JPEG-LS Algorithm Based on FPGA

Cited by 4 publications

References 1 publication

An improved medical image compression technique with lossless region of interest

An improved medical image compression technique with lossless region of interest

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

Face recognition in JPEG compressed domain: a novel coefficient selection approach

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