Morphological Transform for Image Compression

Guzmán, Enrique; Pogrebnyak, Oleksiy; Yáñez, Cornelio; Fernández, Luis

doi:10.1155/2008/426580

Cited by 11 publications

(14 citation statements)

References 32 publications

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“…Embedding function:

T (n) = \sum_{i = 1}^{N 1} d_{1} + \sum_{i = 1}^{N 12} d_{2} = N_{1} * d_{1} + N_{12} * d_{2} = N_{1} * d_{1} + N_{1} * N_{2} * d_{2} = N_{1} * d_{1} + N_{1}^{2} * d_{2}

where k , d , d 1 , and d 2 are constant numbers showing the number of instructions in each loop, and Equation is taken from .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The cover image used for embedding secret message is N × N, so N 1 (number of column) is equal to N 2 (number of rows). The order of morphological function is O(N 2 ), as proved in [24]. Permutation function contains one iteration loop covering two iteration loops after calling GRP function.…”

Section: Time Complexity Of Our Algorithmmentioning

confidence: 96%

“…To map the morphological representation to the original image, a recovery phase is defined in Equation . In this Equation, SB xy is a sub‐block of original image with dimension d × d , and xy indicates which image vectors belong to which image sub‐block; SB xy is obtained after applying the transformation matrix on the morphological representation of a sub‐block, and

v i^{{(italicxy)}_{μ}}

is the μ t h row of the xy th image sub‐block . As it can be seen in Equation , the maximum of additions is used in image recovery.…”

Section: Preliminary Conceptsmentioning

confidence: 99%

“…Then, the modified image is inversely transformed to its original domain. In this category, the most frequent transforms are discrete cosine transform (DCT) [2,[9][10][11][12][13][14], discrete Fourier transform [15,16], discrete wavelet transform (DWT) [17][18][19], discrete contourlet transform [20][21][22], and morphology transform [5][6][7][23][24][25]. Because JPEG compression uses DCT transform, embedding in DCT domain is very famous.…”

Section: Related Workmentioning

confidence: 99%

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A novel image steganography scheme based on morphological associative memory and permutation schema

Nazari

Moghadam

Moin

2014

Security Comm Networks

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Steganography is the art and science of hiding a message in a carrier, in such a manner that no one, except the sender and intended recipient, can guess the existence of the message; it is a form of security through obscurity. Often, steganography algorithms using discrete cosine transform are detectable through steganalysis attacks. To reduce detectability of the stego, we propose an image steganography algorithm in transform domain based on morphological associative memory, permutation approach, and matrix encoding. In our proposed algorithm, in order to insert a secret message in the cover image, first, we map the cover image to a morphological representation containing morphological coefficients, and then bits of secret message are inserted into the image by permutation approach and matrix encoding. Permutation approach leads to unique distribution of secret message in the carrier, and the use of matrix encoding minimizes the number of coefficients needed to be changed because of inserting secret message. In our experiments, we compared the quality of stego, time complexity, and robustness of our method with state‐of‐art image steganography algorithms (F5 and morphological steganography) with an equal payload. The experimental results showed that the proposed method is able to produce insignificant visual distortion compared with other traditional approaches. To test the robustness of our proposed algorithm, we used wavelet‐based and block‐based steganalysis methods. The obtained results showed a high level of robustness of our algorithm against steganalysis attacks. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Embedding function:

T (n) = \sum_{i = 1}^{N 1} d_{1} + \sum_{i = 1}^{N 12} d_{2} = N_{1} * d_{1} + N_{12} * d_{2} = N_{1} * d_{1} + N_{1} * N_{2} * d_{2} = N_{1} * d_{1} + N_{1}^{2} * d_{2}

where k , d , d 1 , and d 2 are constant numbers showing the number of instructions in each loop, and Equation is taken from .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Time Complexity Of Our Algorithmmentioning

confidence: 96%

v i^{{(italicxy)}_{μ}}

is the μ t h row of the xy th image sub‐block . As it can be seen in Equation , the maximum of additions is used in image recovery.…”

Section: Preliminary Conceptsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A novel image steganography scheme based on morphological associative memory and permutation schema

Nazari

Moghadam

Moin

2014

Security Comm Networks

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“…MAMs including fuzzy morphological associative memories (FMAMs) have been extensively analyzed and applied to a variety problems such as pattern recognition and classification [3], [11], prediction [12], vision-based self-localization in robotics [14], [15], image compression [16], color image segmentation [17], and hyperspectral image analysis [18]. The characteristics of the autoassociative morphological memories (AMMs) W XX and M XX include optimal absolute storage capacity and one-step convergence [2], [3].…”

Section: Introductionmentioning

confidence: 99%

An introduction to morphological associative memories in complete lattices and inf-semilattices

Sussner

Medeiros

2012

2012 IEEE International Conference on Fuzzy Systems

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In the mid 1990's, the morphological associative memory (MAM) was introduced as a distributive associative memory model. Since then several extensions of MAMs as well as applications in different domains have appeared in the literature. Just like other morphological neural network models, a MAM performs an elementary operation of mathematical morphology, possibly followed by an activation function, at every node. Generally speaking, a common trait of all distributive MAM models is their foundation in mathematical morphology on complete lattices. Morphological operators in the complete lattice framework come in dual pairs such as dilation/erosion, opening/closing, etc.. Therefore, MAM models also have two versions (denoted using the symbols W and M ) that are tolerant to different types of noise in the input patterns. To overcome this drawback for MAM models, we resort to the more recent theory of mathematical morphology on inf-semilattices whose elementary operators are self-dual. This paper represents a first attempt at formulating an associative memory (AM) in this framework.

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A Comparative Study of Jpeg and Similar Compression Standards for Wireless Communication

Hazowary,

Talukdar,

Dutta

et al. 2024

Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology

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Morphological Transform for Image Compression

Cited by 11 publications

References 32 publications

A novel image steganography scheme based on morphological associative memory and permutation schema

A novel image steganography scheme based on morphological associative memory and permutation schema

An introduction to morphological associative memories in complete lattices and inf-semilattices

A Comparative Study of Jpeg and Similar Compression Standards for Wireless Communication

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