Lossless compression based on hierarchical extrapolation for biomedical imaging applications

Vallathan, G.; Jayanthi, K.

doi:10.1109/icmoce.2015.7489711

Cited by 6 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vallathan et al in paper [22] focus on a lossless image compression that relies on hierarchical extrapolation using Haar transform and the embedded encoding technique. First, the color image is decomposed to Y, C 0 , and C g by applying color transform depicted in (16).…”

Section: Methods Used In Medical Imaging and Telemedicinementioning

confidence: 99%

“…This statement means that an increase in the QS value to a higher compression ratio was registered, but the PSNR value was dropping. Depending on the input image, the results may vary drastically, since for the same QS of 10, the CR results were inside the [6,22] interval. While for a QS of 20, the CR obtained is between 12 and 78, with a PSNR ranging from 37.5 dB to 52.5 dB.…”

Section: Methods Used In Medical Imaging and Telemedicinementioning

confidence: 99%

See 1 more Smart Citation

Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers

Ungureanu,

Negirla,

Korodi

2024

Sensors

View full text Add to dashboard Cite

Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. This paper proposes a study of relevant papers from the last decade which are focused on the selection of a region of interest of an image and on the compression techniques that can be applied to that area. To better highlight the novelty of the hybrid methods, classical state-of-the-art approaches are also analyzed. The current work will provide an overview of classical and hybrid compression methods alongside a categorization based on compression ratio and other quality factors such as mean-square error and peak signal-to-noise ratio, structural similarity index measure, and so on. This overview can help researchers to develop a better idea of what compression algorithms are used in certain domains and to find out if the presented performance parameters are of interest for the intended purpose.

show abstract

Section: Methods Used In Medical Imaging and Telemedicinementioning

confidence: 99%

Section: Methods Used In Medical Imaging and Telemedicinementioning

confidence: 99%

Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers

Ungureanu,

Negirla,

Korodi

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…When a file is decompressed, lossless compression keeps the original file's quality. It is documented that lossless compression is used [2][3][4][5][6]. In this work, to remove the unnecessary portions that need a large amount of memory of digital photographs, the statistical technique is utilized for this task.…”

Section: Introductionmentioning

confidence: 99%

Digital-Image Dimension Reduction Via Analysis of Principal component

Fadhil

Hburi²,

Fleih³

et al. 2022

ejuow

View full text Add to dashboard Cite

An Image with high-resolution is associated with huge size data space because each information of the image is arranged into 2D picture elements' values, each of them containing its associated value of the RGB bits. The depiction of picture data makes it challenging to distribute picture files using the Internet. For Internet users, the time it takes to upload and download photos has all time been the main concern. A high-resolution image takes up more storage space, in addition to the data transit difficulty. The Analysis of Principal Component, or PCA for a brief notation, is a mathematical approach utilized to lessen the data dimensionality. It extracts the main pattern of a linear system using the factoring matrices technique. The objectives of this paper are to see how effective PCA is in reducing digital picture features and to investigate the (feature-reduced) images’ quality on comparison with different values of the variance. As per the synthesizing of the initial research, the dimension or size reduction technique through the Analysis of Principal Component typically involves of 4-important steps: (1) picture-data normalizing (2) matrix of the covariance calculating using picture-data. (3) discovering the picture-data projection (with fewer number of features) to a new basis use the Single Value Decomposition technique (SVD) (4) determining the picture-data projection (with fewer number of characteristics) to a new basis. According to testing results, the PCA approach considerably decreases the size of picture data while sustaining the original picture’s fundamental properties. This approach reduced file size by 35.3 percent for the best feature lowered quality. The upload time of picture files through the Internet has substantially improved, particularly for mobile device downloads.

show abstract

“…Though some encoders have been stated for medical image compression, to the scope of researcher's knowledge, SPIHT is deliberated as an efficient entropy encoder for medical image compression owing to its noticeable features viz. intensive progressive capability, low computational complexity, compact output bit stream with large bit variability and SNR scalability (Sriraam and Shyamsundar, 2011;Vallathan and Jayanthi, 2015).…”

Section: Introductionmentioning

confidence: 99%

An integrated and secured medical data framework for effective tele health applications

Vallathan¹,

Jayanthi

2021

IJAIP

Self Cite

View full text Add to dashboard Cite

The rapid progression of healthcare technologies and transmission strategies makes the system reliable to gain, dispense and manage data over medical devices and as well improves conventional hospital information systems (HIS) to deliver effective healthcare services. When the patient medical information is communicated through wireless network, there exists a high chance of modifying the information. Therefore, a futuristic tele healthcare framework has been proposed to ensure the security, data integrity, quality and minimisation of bandwidth requirements for offering complete healthcare services at reduced cost. The proposed framework encompasses the integration of three modules viz. steganography, compression and encryption. This framework also validates the integrity of ROI, furthermore it ensures better compression ratio. Finally, the whole image is encrypted with modified chaotic map encryption in order to afford complete medical data security. Experimental outcomes demonstrate that the proposed framework offers robustness in terms of security, quality and reliability which alleviate misdiagnosis at the physician end in telemedicine applications.

show abstract

Lossless compression based on hierarchical extrapolation for biomedical imaging applications

Cited by 6 publications

References 15 publications

Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers

Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers

Digital-Image Dimension Reduction Via Analysis of Principal component

An integrated and secured medical data framework for effective tele health applications

Contact Info

Product

Resources

About