A lossless compression approach for mammographic digital images based on the Delaunay triangulation

Author(s):  
L.S. da Silva ◽  
J. Scharcanski
Author(s):  
S. T. Veena ◽  
A. Selvaraj

<p>Today many steganographic software tools are freely available on the Internet, which helps even callow users to have covert communication through digital images. Targeted structural image steganalysers identify only a particular steganographic software tool by tracing the unique fingerprint left in the stego images by the steganographic process. Image steganalysis proves to be a tough challenging task if the process is blind and universal, the secret payload is very less and the cover image is in lossless compression format. A payload independent universal steganalyser which identifies the steganographic software tools by exploiting the traces of artefacts left in the image and in its metadata for five different image formats is proposed. First, the artefacts in image metadata are identified and clustered to form distinct groups by extended K-means clustering. The group that is identical to the cover is further processed by extracting the artefacts in the image data. This is done by developing a signature of the steganographic software tool from its stego images. They are then matched for steganographic software tool identification. Thus, the steganalyser successfully identifies the stego images in five different image formats, out of which four are lossless, even for a payload of 1 byte. Its performance is also compared with the existing steganalyser software tool.</p>


2021 ◽  
Author(s):  
Jennifer J. Valvo ◽  
Jose David Aponte ◽  
Mitch J. Daniel ◽  
Kenna Dwinell ◽  
Helen Rodd ◽  
...  

2018 ◽  
Vol 7 (4.36) ◽  
pp. 419
Author(s):  
V. Beslin Geo ◽  
K. Sakthidasan @ Sankaran ◽  
P. Archana ◽  
M. Umarani

Extraction of hidden text in web images, computer screen images, news, games and e-learning is a very important task in image processing. Compression of digital images leads to poor visual quality of background and text images. Digital images are significantly considered and segmented using DWT into text and background blocks. Huffman coding is used to perform the lossless compression process in the compressed text pixels and the SPIHT algorithm in employed to the compress the background pixels. The result of DWT segmentation shows fringes in the segmented text image. The proposed method uses connected region and edge detection approach which provides a segmented text from digital video stills. The segmented text is converted to binary image using luminance thresholding which leads to fine quality of extracted text. 


Author(s):  
Iryna Victorivna Brysina ◽  
Victor Olexandrovych Makarichev

In this paper, we consider the problem of digital image compression with high requirements to the quality of the result. Obviously, lossless compression algorithms can be applied. Since lossy compression provides a higher compression ratio and, hence, higher memory savings than lossless compression, we propose to use lossy algorithms with settings that provide the smallest loss of quality. The subject matter of this paper is almost lossless compression of full color 24-bit digital images using the discrete atomic compression (DAC) that is an algorithm based on the discrete atomic transform. The goal is to investigate the compression ratio and the quality loss indicators such as uniform (U), root mean square (RMS) and peak signal to noise ratio (PSNR) metrics. We also study the distribution of the difference between pixels of the original image and the corresponding pixels of the reconstructed image. In this research, the classic test images and the classic aerial images are considered. U-metric, which is highly dependent on even minor local changes, is considered as the major metric of quality loss. We solve the following tasks: to evaluate memory savings and loss of quality for each test image. We use the methods of digital image processing, atomic function theory, and approximation theory. The computer program "Discrete Atomic Compression: User Kit" with the mode "Almost Lossless Compression" is used to obtain results of the DAC processing of test images. We obtain the following results: 1) the difference between the smallest and the largest loss of quality is minor; 2) loss of quality is quite stable and predictable; 3) the compression ratio depends on the smoothness of the color change (the smallest and the largest values are obtained when processing the test images with the largest and the smallest number of small details in the image, respectively); 4) DAC provides 59 percent of memory savings; 5) ZIP-compression of DAC-files, which contain images compressed by DAC, is efficient. Conclusions: 1) the almost lossless compression mode of DAC provides sufficiently stable values of the considered quality loss metrics; 2) DAC provides relatively high compression ratio; 3) there is a possibility of further optimization of the DAC algorithm; 4) further research and development of this algorithm are promising.


1998 ◽  
Vol 27 (2) ◽  
pp. 93-96 ◽  
Author(s):  
C H Versteeg ◽  
G C H Sanderink ◽  
S R Lobach ◽  
P F van der Stelt

1999 ◽  
Vol 28 (2) ◽  
pp. 123-126 ◽  
Author(s):  
E Gotfredsen ◽  
J Kragskov ◽  
A Wenzel
Keyword(s):  

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