Image compression based upon Wavelet Transform and a statistical threshold

Nashat, Ahmed A.; Hassan, Nashaat M. Hussain

doi:10.1109/optip.2016.7528492

Cited by 31 publications

(9 citation statements)

References 4 publications

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“…Discrete Wavelet Transform is known to be one of the best compression techniques, especially in image processing (Nashat and Hussain Hassan, 2016). It provides a mathematical tool for encoding information in such a way that it is layered according to the level of detail.…”

Section: Discussionmentioning

confidence: 99%

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data

Ali

Zhuang²,

Ibrahim³

et al. 2022

Front. Artif. Intell.

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This study addresses the challenge represented by the application of deep learning models to the prediction of ocean dynamics using datasets over a large region or with high spatial or temporal resolution In a previous study by the authors of this article, they showed that such a challenge could be met by using a divide and conquer approach. The domain was in fact split into multiple sub-regions, which were small enough to be predicted individually and in parallel with each other by a deep learning model. At each time step of the prediction process, the sub-model solutions would be merged at the boundary of each sub-region to remove discontinuities between consecutive domains in order to predict the evolution of the full domain. This approach led to the growth of non-dynamical errors that decreased the prediction skill of our model. In the study herein, we show that wavelets can be used to compress the data and reduce its dimension. Each compression level reduces by a factor of two the horizontal resolution of the dataset. We show that despite the loss of information, a level 3 compression produces an improved prediction of the ocean two-dimensional data in comparison to the divide and conquer approach. Our method is evaluated on the prediction of the sea surface height of the most energetic feature of the Gulf of Mexico, namely the Loop Current.

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Section: Discussionmentioning

confidence: 99%

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data

Ali

Zhuang²,

Ibrahim³

et al. 2022

Front. Artif. Intell.

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show abstract

“…It has been shown in [17] that DWT is one of the best compression methods. It provides for a mathematical notation to encode information according to the required level of detail.…”

Section: Underlying the Methods Is The Task Of Automating Image Processing In Unmanned Aircraft Systems The Operability Of The Proposed Mmentioning

confidence: 99%

“…The second convolutional layer can be described by formula (16). Due to the boundary effects, the size of the initial matrices decreases according to formula (17). The size of the six output feature maps of a given layer is 18×18 pixels.…”

Section: Fig 5 Elements Of a Convolutional Neural Networkmentioning

confidence: 99%

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

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“…It determines the minimal data required to retain the necessary information. Transform‐based compression techniques are used in many applications [11]. One of the transform‐based compression techniques is wavelet transform (WT) which is one of the most popular lossy methods in the frequency domain [12].…”

Section: Image Compression Techniquesmentioning

confidence: 99%

Performance evaluation of wireless compressed‐image transmission over discrete Fourier transform‐based orthogonal frequency division multiple access system

Al‐Ashwal

Soufy

Al-Kamali

et al. 2022

The Journal of Engineering

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Orthogonal frequency division multiple access (OFDMA) has been attracting much attention in the recent generation of wireless communications to meet the required demands arising from the explosive growth of Internet, multimedia and broadband services. Currently, images transmission is one of the main parts of wireless communication. However, an image file transmission requires sending a large amount of data which consumes a huge bandwidth so that the image must be compressed to reduce the amount of information to be transmitted. The aim of this paper is to evaluate the wireless transmission of the compressed image over discrete Fourier transform (DFT)-based OFDMA DFT-OFDMA system for different compression methods, different modulation schemes and different subcarriers mapping schemes over three wireless channels including SUI3, vehicular A and uniform. The required minimum signal-to-noise ratio (SNR) to recover the transmitted compressed image is determined to evaluate the system performance. For the sake of diversity, nine compression techniques are used in this work. The MATLAB simulator is used to determine the required minimum SNR to recover the transmitted compressed image for each compression method. The results show that the performance of the set partitioning in hierarchical trees three-dimensional for true colour images (SPIHT-3D) method is nearly better than that of other compression methods, especially when the QPSK and interleaved system are considered.

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Image compression based upon Wavelet Transform and a statistical threshold

Cited by 31 publications

References 4 publications

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Performance evaluation of wireless compressed‐image transmission over discrete Fourier transform‐based orthogonal frequency division multiple access system

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