Image Data Compression Research Articles

Image Data Compression and Decompression Unit Considering Brightness Sensitivity

Image Data Compression and Decompression Unit Considering Brightness Sensitivity

Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network

Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The approximated observation sparse SAR imaging method relieves the computation pressure, but it needs to manually set the parameters to solve the optimization problem. Thus, several deep learning (DL) SAR imaging methods have been used for scene recovery, but many of them employ dual-path networks. To better leverage the complex-valued characteristics of echo data, in this paper, we present a novel complex-valued convolutional neural network (CNN)-based approximated observation sparse SAR imaging method, which is a single-path DL network. Firstly, we present the approximated observation-based model via the chirp-scaling algorithm (CSA). Next, we map the process of the iterative soft thresholding (IST) algorithm into the deep network form, and design the symmetric complex-valued CNN block to achieve the sparse recovery of large-scale scenes. In comparison to matched filtering (MF), the approximated observation sparse imaging method, and the existing DL SAR imaging methods, our complex-valued network model shows excellent performance in image quality improvement especially when the used data are down-sampled.

Design Polynomial IIR Digital Filters of the Integer Parameters Space Use to Compress Image Data

Polynomial IIR digital filters play a crucial role in the process of image data compression. The main purpose of designing polynomial IIR digital filters of the integer parameters space and introduce efficient filters to compress image data using a singular value decomposition algorithm. The proposed work is designed to break down the complex topic into bite-sized pieces of image data compression through the lens of compression image data using Infinite Impulse Response Filters. The frequency response of the filters is measured using a real signal with an automated panoramic measuring system developed in the virtual instrument environment. The analysis of the output signal showed that there are no limit cycles with a maximum radius of poles of 0.96 in the polynomial bandpass filters. Thus, all the functional requirements for the Integer Parameters Space of the proposed polynomial IIR digital filters were met. The results showed that the data compression and size reducing of an image file is processed without significantly impacting of visual quality. This is achieved by removing redundant or unnecessary information from the image while preserving the important details which removes unnecessary data to make the file smaller and more manageable.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

Application of Multi-Level Thresholding Fuzzy Entropy and Differential Evolution Techniques to Medical Image Compression

Each storage medium possesses a capacity that is vital to technological progress in supporting internet speed for downloading extensive data. Effective storage media management includes compressing image data to optimize available space utilization for efficient usage. Digital processing of image data for each patient over a specific duration is crucial. This study proposes medical image compression utilizing multi-level thresholding employing the fuzzy entropy and differential evolution (FE-DE) algorithms. The compression results are subsequently assessed using the Peak Signal to Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) to appraise the algorithm's performance and the quality of the compressed images. Employing Fuzzy Compression yields increasing PSNR, SSIM, and FSIM values as the threshold level rises, with the highest values achieved at threshold level 40. The increase in value is in line with the visual quality and compressed image produced.

Panoramic Radiograph Generation and Image Reconstruction from Latent Vectors Using a Generative Adversarial Network.

In this study, StyleGAN2 was trained with panoramic radiographs, and original images were projected into the latent space of StyleGAN2. The resulting latent vectors were input into StyleGAN2, and corresponding images were generated to reconstruct the original images. The original and reconstructed images were evaluated by pediatric dentists and found to be similar. Our results suggest that StyleGAN2 could be applied to the anonymization and data compression of medical images.

Optical cryptosystem based on computational ghost imaging and nonlinear authentication.

We propose an optical encryption system that combines computational ghost imaging (CGI) with image authentication to enhance security. In this scheme, Hadamard patterns are projected onto the secret images, while their reflected light intensities are captured using a bucket detector (BD). To further strengthen the security of the collected secret data, we encrypt it as a series of binary matrices serving as ciphertext. During the authentication key generation, these encoded binary matrices serve as illumination patterns in the CGI system for a non-secret image, which is used as a reference image for authentication. The data captured by the BD is then binarized to generate the authentication key. Upon successful authentication, the receiver obtains the decryption keys. This method achieves both data compression for secret images and enhanced security during information transmission. We validate the feasibility of this method through computer simulations and optical experiments.

JPEG-compatible Joint Image Compression and Encryption Algorithm with File Size Preservation

Joint image compression and encryption algorithms are intensively investigated due to their powerful capability of simultaneous image data compression and sensitive information protection. Unfortunately, most of the existing algorithms suffered from either poor compression efficiency or weak encryption strength, making them vulnerable to cryptanalysis. To address these limitations, we propose a chaos-based JPEG-compatible joint image compression and encryption algorithm. We separate the luminance and chrominance coefficients to preserve file size and encrypt the discrete cosine transform (DCT) coefficients in parallel. The proposed inter-block DC encryption strategy achieves high encryption intensity based on the permutation-substitution structure. In addition, we apply both inter- and intra-block permutations to AC coefficients and strengthen the encryption using an inter-block substitution for non-zero AC coefficients. The results of security and performance analyses demonstrate that the proposed algorithm offers robust encryption of image data while maintaining compression efficiency for real-time transmission.

A scheme for encrypted image data compression

A hybrid approach of image encryption and compression can give highly secured and compressed image data which can be easily transmitted through unsecured and bandwidth constrained channels. This paper proposes a mechanism which handles image encryption prior to image compression. The work is basically divided into two parts. The first part focuses on image encryption approach in which Roman numeral logic and pixel scrambling logic is applied to image data. The second part focuses on compression of encrypted image data using Huffman’s encoding. The proposed approach is lightweight, easy to understand and implement. Results are obtained using standard dataset and it is observed that 16%-18% compression is obtained after encryption.

基于高光谱成像和卷积神经网络的欧李果实缺陷检测

In order to perform highly effective identification of external defects and increase the additional value of Cerasus Humilis fruits, this study used hyperspectral imaging technology to collect information on intact and defective Cerasus Humilis fruits. Based on the full transition spectrum, partial least squares discriminant analysis (PLS-DA) and back propagation neural networks (BPNN) were used to establish a discriminative model. The competitive adaptive reweighted sampling (CARS) was used to extract feature wavelengths, principal component analysis was used for data compression of single band images, BPNN and convolutional neural networks (CNN) were used for defect Cerasus Humilis fruits recognition of principal component images. The results showed that the overall detection accuracy of PLS-DA and BPNN models based on wavelength spectral information were 83.81% and 85.71%, respectively. BPNN was used to establish the calibration model based on the selected characteristic wavelengths by CARS, the accuracy rate was 90.47%. The classified accuracy of CNN model based on principal component images was 93.33%, which was obviously better than that of BPNN model at 83.81%. The research shows that the CNN model was successfully applied to the detection of Cerasus Humilis fruits defects using hyperspectral imaging. This study provides a theoretical basis for the development of fruit grading and sorting equipment.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR SOLVING PROBLEMS OF ACOUSTIC NONDESTRUCTIVE TESTING (REVIEW)

Nowadays, artificial neural networks are finding more and more applications in various fields of human activity. This paper reviews the current state of research and development in the field of application of artificial neural networks for solving problems in acoustic non-destructive testing. The areas of application of neural networks include processing of inspection data, flaw detection, determination of flaw parameters, and determination of material properties. Test data processing includes increasing the signal-to-noise ratio of results, data compression, restoration and processing of flaw images. Within flaw parameter determination, flaw sizing and flaw type identification tasks have been solved using artificial neural networks. One of the most challenging problems related to the introduction of artificial neural networks in acoustic nondestructive testing is the small amount of inspection data that complicates the training of artificial networks. The ways to solve this problem are data exchange between organizations and standardization of inspection results, data augmentation and application of computer modeling.

Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.

Retracted: Multimedia Image Data Compression Based on Wavelet Analysis

Retracted: Multimedia Image Data Compression Based on Wavelet Analysis

An efficient image compression architecture based on optimized 9/7 wavelet transform with hybrid post processing and entropy encoder module

Satellite communication is popular due to the advancement in storing and transmitting satellite images. Image compression can be defined as the science of lowering the number of bits necessary to represent an image. In satellite image processing, image degradation is considered a challenging problem. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is the transformation-dependent image compression method developed especially for usage in on-board space platforms. It holds de-correlation, quantization and entropy encoding phases. This paper introduces a new optimized memory organization for Discrete Wavelet Transform (DWT) to perform spatial de-correlation with fewer memory requirements on an FPGA device. Also, the proposed optimized DWT is integrated with a hybrid post-processing and entropy encoder module to reduce the spatial redundancies between the wavelet coefficients and compress the de-correlated data with high compression performance. A high-throughput hardware implementation of the Binary arithmetic entropy Coder (BAEC) is also provided to perform lossless compression with low implementation complexity. To provide the convenience and compactness, the proposed system will be implemented in the Xilinx working platform by developing a Verilog code. The proposed model is then evaluated on the Arty Z7–20 development board. The proposed approach is analyzed on different performance parameters such as throughput and frequency. The proposed design allowed a maximum operating frequency of 250 MHz, leading to a throughput of 156.25 Msamples/sec on Zynq. In addition, the structure of the proposed method overtakes the conventional design in terms of memory requirements and area.

Quantum Approach to Image Data Encoding and Compression

High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> classical data values are encoded into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.

A Chaotic Compressed Sensing-Based Multigroup Secret Image Sharing Method for IoT With Critical Information Concealment Function

Nowadays, the requirements for image data sharing among participant nodes of the Internet of Things (IoT) are constantly emerging. And thanks to the popularity of digital cameras and the development of digital photography technologies, the processes of data sharing commonly concern substantial amounts of data that may contain critical or private information. So, how to design a secure and efficient secret image sharing (SIS) method suitable for IoT apparatuses is attracting ever-increasing attention. With the aim of simultaneously realizing SIS, image data compression, and critical information or privacy protection, this article proposes a multigroup SIS method based on the model of compressed sensing (CS) and chaos theory. In the proposed method, participants of SIS are classified into two groups with different authorization levels. Solely members of full-authorized groups could reconstruct secret images with visible critical sectors. Members of restricted-authorized groups, however, could merely obtain reconstruction results with critical sectors concealed. The CS model is introduced to the proposed method to accomplish image data compression and applications of the chaos theory contribute to the secure data transmission. Experimental simulations and theoretical analyses are performed to discuss the feasibility, flexibility, and security of the proposed method.

Image parallel block compressive sensing scheme using DFT measurement matrix

Compressive sensing (CS)-based image coding has been widely studied in the field of image processing. However, the CS-based image encoder has a significant gap in image reconstruction performance compared with the conventional image compression methods. In order to improve the reconstruction quality of CS-based image encoder, we proposed an image parallel block compressive sensing (BCS) coding scheme, which is based on discrete Cosine transform (DCT) sparse basis matrix and partial discrete Fourier transform (DFT) measurement matrix. In the proposed parallel BCS scheme, each column of an image block is sampled by the same DFT measurement matrix. Due to the complex property of DFT measurement matrix, the compressed image data is complex. Then, the real part and imaginary part of the resulting BCS data are quantized and transformed into two bit streams, respectively. At the reconstruction stage, the resulting two bit streams are transformed back into two real signals using inverse quantization operation. The resulting two real signals are combined into one complex signal, which is served as the input data of the CS reconstructed algorithm. The theoretical analysis based on minimum Frobenius norm method demonstrates that the proposed DFT measurement matrix outperforms the other conventional measurement matrices. The simulation results show that the reconstructed performance of the proposed DFT measurement matrix is better than that of the other conventional measurement matrices for the proposed parallel BCS. Specifically, we analyzed the impact of quantization on the reconstruction performance of CS. The experiment results show that the effect of the quantization on reconstruction performance in BCS framework can nearly be ignored.

Multimedia Image Data Compression Based on Wavelet Analysis

In order to improve the compression technology of multimedia image data, a compression method of multimedia image data based on wavelet analysis was proposed. By combining Fourier transform and wavelet packet multithreshold denoising method, the image data was denoised and compressed through the transform of wavelet analysis and multithreshold processing, so as to realize the compression of image data with high quality. The experimental results showed that it was effective to analyze signal singularity, singularity position, and singularity degree by using wavelet analysis. And the signal mutation position near t = 0 could be seen clearly from the wavelet coefficient diagram, while the Fourier transform diagram did not provide any information about when the frequency mutation occurred. It was concluded that the method of multimedia image data compression based on wavelet analysis could promote the development of multimedia image data compression technology effectively.

A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes. Therefore, BBNs have high requirements in energy efficiency, data security, and privacy protection. In this paper, we propose a secure and efficient data transmission method for sensor nodes within BBNs that is based on the perception of chaotic compressive sensing. This method can simultaneously accomplish data compression, encryption, and critical information concealment during the data sampling process and provide various levels of reconstruction qualities according to the authorization level of receivers. Simulation and experimental results demonstrate that the proposed method could realize data compression, encryption, and critical information concealment for images that are transmitted within BBNs. Specifically, the proposed method could enhance the security level of data transmission by breaking the statistical patterns of original data, providing large key space and sensitivity of the initial values, etc.

Pengaruh Peningkatan Kualitas Citra Menggunakan Modifikasi Kontras Pada Kompresi Data RLE

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.