Applications of digital straight segments to economical image encoding

Conventionally, magnetic resonance imaging (MRI) is performed by pulsing gradient coils, which invariably leads to strong acoustic noise, patient safety concerns due to induced currents, and costly power/space requirements. This modeling study investigates a new silent, gradient coil-free MR imaging method, in which a radiofrequency (RF) coil and its nonuniform field (B1+) are mechanically rotated about the patient. The advantage of the rotatingB1+field is that, for the first time, it provides a large number of degrees of freedom to aid a successfulB1+image encoding process. The mathematical modeling was performed using flip angle modulation as part of a finite-difference-based Bloch equation solver. Preliminary results suggest that representative MR images with intensity deviations of <5% from the original image can be obtained using rotating RF field approach. This method may open up new avenues towards anatomical and functional imaging in medicine.

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Image Compression Using Fractal Functions

Fractal and Fractional ◽

10.3390/fractalfract5020031 ◽

2021 ◽

Vol 5 (2) ◽

pp. 31

Author(s):

Olga Svynchuk ◽

Oleg Barabash ◽

Joanna Nikodem ◽

Roman Kochan ◽

Oleksandr Laptiev

Keyword(s):

Image Compression ◽

Spatial Data ◽

Information Technologies ◽

Radio Channel ◽

Continuous Functions ◽

Interesting Property ◽

Geographic Information ◽

Fractal Image Compression ◽

Image Encoding ◽

Fractal Functions

The rapid growth of geographic information technologies in the field of processing and analysis of spatial data has led to a significant increase in the role of geographic information systems in various fields of human activity. However, solving complex problems requires the use of large amounts of spatial data, efficient storage of data on on-board recording media and their transmission via communication channels. This leads to the need to create new effective methods of compression and data transmission of remote sensing of the Earth. The possibility of using fractal functions for image processing, which were transmitted via the satellite radio channel of a spacecraft, is considered. The information obtained by such a system is presented in the form of aerospace images that need to be processed and analyzed in order to obtain information about the objects that are displayed. An algorithm for constructing image encoding–decoding using a class of continuous functions that depend on a finite set of parameters and have fractal properties is investigated. The mathematical model used in fractal image compression is called a system of iterative functions. The encoding process is time consuming because it performs a large number of transformations and mathematical calculations. However, due to this, a high degree of image compression is achieved. This class of functions has an interesting property—knowing the initial sets of numbers, we can easily calculate the value of the function, but when the values of the function are known, it is very difficult to return the initial set of values, because there are a huge number of such combinations. Therefore, in order to de-encode the image, it is necessary to know fractal codes that will help to restore the raster image.

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Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.488-489.1587 ◽

2012 ◽

Vol 488-489 ◽

pp. 1587-1591

Author(s):

Amol G. Baviskar ◽

S. S. Pawale

Keyword(s):

Feature Extraction ◽

Image Compression ◽

Low Complexity ◽

Contractive Mapping ◽

Fractal Image Compression ◽

Compression Technique ◽

Image Encoding ◽

Fractal Image ◽

Coding Method ◽

The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

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An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.738-739.598 ◽

2015 ◽

Vol 738-739 ◽

pp. 598-601

Author(s):

Han Yang Zhu ◽

Xin Yu Jin ◽

Jian Feng Shen

Keyword(s):

Computational Complexity ◽

Prediction Model ◽

Image Coding ◽

Region Of Interest ◽

Image Encoding ◽

Large Size ◽

Learning Machine ◽

Intractable Problem ◽

Encoding Efficiency ◽

Better Than

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

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SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION

Fractals ◽

10.1142/s0218348x09004405 ◽

2009 ◽

Vol 17 (02) ◽

pp. 149-160 ◽

Cited By ~ 9

Author(s):

SHIGUO LIAN ◽

XI CHEN ◽

DENGPAN YE

Keyword(s):

Image Coding ◽

Parameter Distribution ◽

Self Similarity ◽

Image Encoding ◽

Coding Scheme ◽

Fractal Image ◽

Fractal Image Coding ◽

Fractal Parameters ◽

Fractal Encoding ◽

Computational Resources

In recent work, various fractal image coding methods are reported, which adopt the self-similarity of images to compress the size of images. However, till now, no solutions for the security of fractal encoded images have been provided. In this paper, a secure fractal image coding scheme is proposed and evaluated, which encrypts some of the fractal parameters during fractal encoding, and thus, produces the encrypted and encoded image. The encrypted image can only be recovered by the correct key. To maintain security and efficiency, only the suitable parameters are selected and encrypted through investigating the properties of various fractal parameters, including parameter space, parameter distribution and parameter sensitivity. The encryption process does not change the file format, keeps secure in perception, and costs little time or computational resources. These properties make it suitable for secure image encoding or transmission.

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