Embedded Image Compression Algorithm and FPGA Implementation Based on BP Neural Networks

2011 ◽  
Vol 65 ◽  
pp. 415-418
Author(s):  
Guang Ming Li ◽  
Zhen Qi He
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Compression Ratio ◽  
Bp Neural Network ◽  
Stable Operation ◽  
High Compression Ratio ◽  
The Neural Network ◽  
Embedded Image ◽  
Image Compression Algorithm

At present, because more embedded image compressions are single, various compression methods have not transplant to embedded equipment. In this paper, A BP neural network based image compression methods have been proposed. The neural network is trained more and more, and obtained a set of weights and thresholds usefully. Then, use the FPGA to achieve, In the FPGA, using the framework of soft-core Nios Ⅱ way. Ultimately, compression program written using Verilog and burned into the FPGA. Experiments show that the system has the advantages of high compression ratio, small size, and can stable operation.

scholarly journals Image Compression Using Neural Networks: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 135-153
Author(s):  
Haval Tariq Sadeeq ◽  
Thamer Hassan Hameed ◽  
Abdo Sulaiman Abdi ◽  
Ayman Nashwan Abdulfatah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Image Coding ◽  
Image Data ◽  
Coding System ◽  
Memory Space ◽  
Coding Efficiency ◽  
Huge Data ◽  
The Neural Network

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

scholarly journals Backpropagation Neural Network Implementation for Medical Image Compression

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kamil Dimililer
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Compression Ratio ◽  
Haar Wavelet ◽  
Compression Method ◽  
High Compression Ratio ◽  
Haar Wavelet Transform ◽  
X Ray ◽  
Ideal Image ◽  
And Storage

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

An Image Compression Algorithm Based on Quantization and DWT-BP Neural Network

2021 ◽  
Author(s):  
Shumei Liu ◽  
Hongmei Yang ◽  
Jeng-Shyang Pan ◽  
Tao Liu ◽  
Bin Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Bp Neural Network ◽  
Compression Algorithm ◽  
Image Compression Algorithm

SAR Image Compression by Using Neural Network in Wavelet Domain

2013 ◽  
Vol 760-762 ◽  
pp. 1599-1603 ◽  
Author(s):  
Min Li ◽  
Xiao Ying Zhang ◽  
Lin Ju Lu
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Human Vision ◽  
Speckle Reduction ◽  
Sar Image ◽  
Wavelet Domain ◽  
The Neural Network ◽  
Processing Ability ◽  
Image Compression Algorithm ◽  
Self Learning

According to the characteristic of SAR image containing the multiplicative speckle, a kind of neural network image compression algorithm in wavelet domain was proposed. Wavelet transform can well reflect the characteristics of human vision, but the neural network has self-learning, adaptive, robust, highly parallel processing ability and generalization ability. The wavelet and neural network together in SAR image compression compared with other encoding methods, has obvious advantage. Compared with block-DCT algorithm and sub-band DWT method, this algorithm preserves more advantage in speckle reduction and image details keeping, the compressed image with visual features the best.

scholarly journals Artificial neural network applications for data compression in video data transfer protocols

Connectivity ◽  
2020 ◽  
Vol 146 (4) ◽  
Author(s):  
G. Ya. Kis ◽  
◽  
V. M. Cherevyk ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Data Compression ◽  
Video Compression ◽  
Data Transfer ◽  
Video Data ◽  
Current State ◽  
The Neural Network ◽  
Artificial Neural

The article describes the current state of data transfer protocols and methods of image and video compression through the use of artificial neural networks, namely convolutional multilayer networks and deep structured learning. Based on recent publications, a comparative analysis of the performance of classical compression methods and methods based on neural networks was performed. The most effective are those compression methods which are based on decorrelation transforms, namely discrete cosine (JPEG standard) and wavelet (JPEG-2000 standard) transforms. The transform coefficients have a well-understood physical content of spatial frequencies and can be further quantized for a more optimal representation of components that are less important for human perception. The HEVC standard guarantees a more efficient image compression scheme that further takes advantage of the similarity of adjacent blocks and uses interpolation (intracoding). Based on the HEVC standard, the BPG (better portable graphics) format was developed to be used on the Internet as an alternative to JPEG, which is much more efficient than other standards. An overview of the current state of open standards, provided in the article, gives an explanation of what properties of neural networks can be applied to image compression. There are two approaches towards the compression using neural networks: in case of the first approach neural network is used as a part of an existing algorithm (hybrid coding), and in case of the second approach the neural network gives a concise representation of the data (compression network). The final conclusions were made as regards to the application of these algorithms in H.265 protocol (HEVC) and the possibility of creating a new protocol which is completely based on the neural network. Protocols using neural network show better results during image compression, but are currently hard to be subjected to standardization in order to obtain the expected result in case of different network architects. We may expect and predict an increase in the need for video transmission in the future, which will bump into the imitating nature of classical approaches. At the same time, the development of specialized processors for parallel data processing and implementation of neural networks is currently underway. These two factors indicate that neural networks must be embedded into the industrial data standards.

scholarly journals Development of a virtual management system for the cutting process using neural networks

2018 ◽  
Vol 224 ◽  
pp. 01015
Author(s):  
Irina Ovsyanikova ◽  
Aleksandr Tarapanov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Model ◽  
Stable Operation ◽  
Network Configuration ◽  
The Neural Network ◽  
Input Parameters ◽  
Virtual Management ◽  
Processing Control ◽  
Control Criteria

The article deals with the problems of choosing the neural network configuration for blade processing control. The analysis of input parameters is carried o7ut and the virtual representation of cutting’s technological process is simulated. A mathematical control model that allows to mutually transform processes depending on the control criteria is proposed. The configuration of the neural network, that allows to provide the most productive and stable operation with insufficient and contra dictionary input parameters is established.

The Application of BP Neural Networks in Cable-Stayed Construction Control

2014 ◽  
Vol 584-586 ◽  
pp. 2001-2005
Author(s):  
Xi Fen Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Technical Information ◽  
Main Beam ◽  
Construction Process ◽  
Cable Stayed Bridge ◽  
Input And Output ◽  
The Neural Network

Through the related technical information of the monitoring of bridge construction, this is to determine BP neural network's input and output parameters, to establish the neural network fore-casted model, and to carry through the prediction to the main beam of deflection. In the application, the data of deflection deviation has generated through the cast-in-place segmental cantilever to train the BP neural network. The deviations which are produced in the pre-run neural networks predict the follow-up segment in construction, and then simulation control in the cable-stayed bridge is realized in construction process.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

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