Efficiency Evaluation of Group Signals Transformation for Wireless Communication in V2X Systems

Ivanov, Vladislav; Lopukhova, Ekaterina A.; Voronkov, Grigory S.; Grakhova, Elizaveta P.

doi:10.1109/usbereit56278.2022.9923401

Cited by 4 publications

(2 citation statements)

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“…This makes it possible to take into account their mutual connection and simplifies the coding scheme in a multi-channel system, that is, the codec structure. The performance of CGST has already been demonstrated for processing low-speed data [18], its effectiveness in increasing energy efficiency by reducing the amount of transmitted information (shortening the codeword length) was demonstrated in [19].…”

Section: Of 11mentioning

confidence: 99%

A Novel Image Compression Method Based on Coordinated Group Signal Transformation

Lopukhova,

Voronkov,

Kuznetsov

et al. 2024

Preprint

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The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

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Section: Of 11mentioning

confidence: 99%

A Novel Image Compression Method Based on Coordinated Group Signal Transformation

Lopukhova,

Voronkov,

Kuznetsov

et al. 2024

Preprint

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“…Coordinated group signal transformation (CGST) is a non-transform-based approach that reduces the average power of source signals through signal prediction. It has already been proven to be effective for processing low-speed data [41,42]. Unlike existing difference coding methods that predict separate signals [43,44], CGST predicts multiple correlated signals together, simplifying the coding scheme in a multi-channel system, such as the codec structure.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

Lopukhova,

Voronkov,

Kuznetsov

et al. 2024

Applied Sciences

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This paper introduces a new method for compressing images in energy-starved systems, like satellites, unmanned aerial vehicles, and Internet of Things nodes, which is based on coordinated group signal transformation (CGST). The transformation algorithm is a type of difference coding and may be classified as a non-transform-based image-compression method. CGST simplifies the difference signal conversion scheme using a single group codec for all signals. It considers color channels as correlated signals of a multi-channel communication system. The performance of CGST was evaluated using a dataset of 128 × 128 pixel images from satellite remote sensing systems. To adapt CGST to image compression, some modifications were introduced to the algorithm, such as fixing the procedure of the difference signals calculation to prevent any “zeroing” of brightness and supplementing the group codec with a neural network to improve the quality of restored images. The following types of neural networks were considered: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, fully connected neural networks are recommended if the goal is to minimize processing delay time. These networks have a response time of 13 ms. Conversely, suppose the priority is to improve quality in cases where delays are not critical. In that case, convolution neural networks in the Fourier space should be used, providing an image compression ratio of 4.8 with better minimum square error and Mikowsky norm values than JPEG with the same compression ratio.

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Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions

Rammohan

2023

Vehicular Communications

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Efficiency Evaluation of Group Signals Transformation for Wireless Communication in V2X Systems

Cited by 4 publications

References 0 publications

A Novel Image Compression Method Based on Coordinated Group Signal Transformation

A Novel Image Compression Method Based on Coordinated Group Signal Transformation

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions

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