Cosine Number Transform based hybrid image compression-encryption

Ponuma, R.; Aarthi,; Amutha, R.

doi:10.1109/wispnet.2016.7566114

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Where the signal is represented as low and high band coefficients, then finally the different image reconstruction schemes are used for low and high frequency components. Ponuma et al [8] presented a hybrid compression scheme to decrease the computational complexity of the system as well as the memory requirements. It uses the arithmetic and block based CS scheme to reduce the memory requirements.…”

Section: Review Of Literaturementioning

confidence: 99%

RM2 IC: Performance Analysis of Region based Mixed-mode Medical Image Compression

Lakshminarayana¹,

Sarvagya²

2017

IJIGSP

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Abstract-The medical data science has been changing from conventional analog to more powerful digital imaging systems for some time. These imagining systems produced images in digital form. As digital technology evolves and exceeds the capability of analog imaging devices, so too does the expansion in the range of applications for image guided surgical and diagnostic systems. The optimization of bandwidth and storage are the major issues in image processing technology. The Compressive Sensing (CS) algorithm can become prominent tool for these issues because it can sample the signal with much lesser sample rate than twice of the maximum frequency of the signal and reconstruct the signal similar to the original signal. This paper, presents a novel scheme Region based Mixed-mode Medical Image Compression (RM 2 IC). Here, the region of interest is compressed with lossless hybrid compression methods and the non-region of interest is com-pressed with lossy hybrid CS algorithm. RM 2 IC is compared with different existing hybrid compression methods and it outperforms better visual perceptional quality of reconstructed image and reduces the compression rate. The performance analysis is done based on PSNR, MSE and compression ratio.

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Section: Review Of Literaturementioning

confidence: 99%

RM2 IC: Performance Analysis of Region based Mixed-mode Medical Image Compression

Lakshminarayana¹,

Sarvagya²

2017

IJIGSP

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“…Originally, such transforms were mainly used to efficiently compute error-free convolutions [18,[21][22][23]; this possibility performed a fundamental role at that time, since the hardware could work with finite precision arithmetic only. In recent years, the referred transforms have been used predominantly in applications related to information security, which include image encryption, watermarking and signal processing in the encrypted domain, for instance [3,11,14,17,19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Defining a Hilbert Number Transform

Lima

Gondim

Souza

2021

Circuits Syst Signal Process

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In this brief, we introduce a new definition for the Hilbert number transform (HNT). Our approach employs concepts related to trigonometry over finite fields and uses as a starting point a specific definition for the Fourier number transform (FNT); one demonstrates that such an FNT allows to define an HNT which is analogous to the real-valued version of the corresponding transform. Additionally, we present a method to obtain the proposed HNT from the recently proposed steerable Fourier number transform.

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“…In hybrid mode compressed algorithm, where it integrates both the ℓ1-norm as well as ℓ0-norm algorithm and results better visual perceptional quality of reconstructed images. Analogous kind of work was done by [10] improves the reconstruction quality of image using the block compressive sensing schemes. In [11] analyzed the compressive sensing method and its specific requirements on medical images using deterministic compressed sensing system.…”

Section: Introductionmentioning

confidence: 99%

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

Lakshminarayana¹,

Sarvagya²

2018

IJECE

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The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.

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Cosine Number Transform based hybrid image compression-encryption

Cited by 11 publications

References 11 publications

RM2 IC: Performance Analysis of Region based Mixed-mode Medical Image Compression

RM2 IC: Performance Analysis of Region based Mixed-mode Medical Image Compression

A Novel Approach for Defining a Hilbert Number Transform

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

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