Calculation of Image Transmission Partial Spectrum

Bastrychkin, Aleksandr S.; Kostrov, Boris; Grinchenko, Natalya N.; Svetlov, Gennady V.

doi:10.1109/meco49872.2020.9134144

Cited by 2 publications

(2 citation statements)

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“…where 𝐆 is a matrix in spatial representation, 𝑔 𝑖,𝑗 is the brightness value of one pixel, 𝑁 × 𝑁 is the image size. Data transmission via communication channels takes place at the physical level, while it is more convenient to represent discrete values of image brightness in a spatial-spectral form, which is formed in accordance with a certain function [2,3]. In the conducted study, Walsh functions [4] were used as non-trigonometric orthogonal basis functions, which were superimposed on separate blocks of values with a size of 32 × 32 (2) to decrease the value of the spectral component that reduces the amount of memory needed to store the image in a spatially spectral form.…”

Section: Forming Transmission Vectormentioning

confidence: 99%

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Parallel computations in problems of reconstruction of distorted images in spatial-spectral form

Kostrov¹,

Grinchenko²,

Vyugina³

et al. 2023

Proceedings of ISP RAS

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The object of the study is the image processing process in preparation for data transmission, as well as subsequent recovery. The subject of the study is the application of parallel computing in image processing tasks. The purpose of the article is to study the method of image reconstruction with correction of distortion of the transmission vector based on the input of a value in the trend of its neighbors. The relevance of this topic is determined by the need to effectively perform the operations preceding and following the transmission over the communication channel. During the experimental part, the results were obtained in the form of execution time values with sequential execution and using parallel calculations, which gave the expected increase in speed-up. Since the image consists of unrelated parts, it can be successfully processed by applying data parallelization.

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Section: Forming Transmission Vectormentioning

confidence: 99%

“…Each of the individual blocks is transformed to a spatially spectral form by one method, which means that the computational process can be parallelized for each submatrix of pixels. As a result of the transformation, a matrix is obtained representing the image in the spatial-spectral form (3).…”

Section: Forming Transmission Vectormentioning

confidence: 99%