A New Preprocessing Algorithm Used in Color Image Compression

Wu, Peng; Xie, Kai; Yu, HuoQuan; Zheng, Yunping; Yu, WenMao

doi:10.1007/978-3-642-29387-0_71

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…The irreversible transform ICT ( Irreversible Color Transform) is used in the case of a lossy compression . RCT reversible color transform ( Reversible Color Transform ) used in the case of lossless compression or lossy , is a approximation of the previous transformed [ 6 ].The RGB to YCbCr is performed respecting to eq (1).…”

Section: A Color Space Conversionmentioning

confidence: 99%

“…In this state, the original image X is decomposed into two different parts, Xe(n)=X(2n) and X 0 (n) =X(2n+1) that present respectively all even-indexed and oddindexed pixels of image X [6].…”

Section: ) Splittingmentioning

confidence: 99%

“…In order to evaluate medical image compression by our algorithm, The PSNR results obtained are compared with the existing techniques namely JPEG codec [6]. This paper is organized as follows: Sect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Alrababah¹,

Al-Marghirani²

2016

ijacsa

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Abstract-The medical image processing process is one of the most important areas of research in medical applications in digitized medical information. A medical images have a large sizes. Since the coming of digital medical information, the important challenge is to care for the conduction and requirements of huge data, including medical images. Compression is considered as one of the necessary algorithm to explain this problem. A large amount of medical images must be compressed using lossless compression. This paper proposes a new medical image compression algorithm founded on lifting wavelet transform CDF 9/7 joined with SPIHT coding algorithm, this algorithm applied the lifting composition to confirm the benefit of the wavelet transform. To develop the proposed algorithm, the outcomes compared with other compression algorithm like JPEG codec. Experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI images.

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Section: A Color Space Conversionmentioning

confidence: 99%

Section: ) Splittingmentioning

confidence: 99%

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Implementation of Novel Medical Image Compression Using Artificial Intelligence

Alrababah¹,

Al-Marghirani²

2016

ijacsa

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“…Lossy techniques are used when loss can be accepted, and lossless techniques are used for applications that cannot afford any loss of information (e.g. in the medical field) [22].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Sreenivasulu

Varadarajan

2018

Journal of Intelligent Systems

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Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

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“…In this file size of medical images increases as the resolution demand increases and eventually issues may arise during transmission and communication where network resource is a constraint. Besides that, archiving these for post processing or medical act legal requirements will be a daunting task because of the large file size [2].…”

Section: Introductionmentioning

confidence: 99%

Medical images Compression using convolutional neural network with LWT

Shukla¹,

Srivastava²

2018

IJETR

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Abstract-In compression of medical image using convolutional neural network trained with the back-propagation algorithm and lefted wavelet transformation is proposed to compress high quality medical images. It gives much better result as compared to feed-forward neural network . Medical image processing process is one of the most important section of research in medical applications in digital medical information. In this new approach , a three hidden layer convolutional neural network (CNN) is applied directly as the main compression algorithm to compress an MRI, X-ray, computer tomography images. After training with sufficient sample images, the compression process will be carried out on the target image. The coupling weights and activation values of each neuron in the hidden layer will be stored after training. Compression is achieved by using smaller number of hidden neurons as compare to the number of image pixels due to lesser information being stored. experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested Experimental results show that the CNN is able to achieve comparable compression performance to popular existing medical image compression schemes such as JPEG2000 and JPEG-LS.The Wavelet-SPIHT algorithm provides PSNR very important values for MRI and CT scan images.

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A New Preprocessing Algorithm Used in Color Image Compression

Cited by 6 publications

References 8 publications

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Implementation of Novel Medical Image Compression Using Artificial Intelligence

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Medical images Compression using convolutional neural network with LWT

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