Compression Encryption Research Articles

GeohashTile: Vector Geographic Data Display Method Based on Geohash

In the development of geographic information-based applications for mobile devices, achieving better access speed and visual effects is the main research aim. In this paper, we propose a new geographic data display method based on Geohash, namely GeohashTile, to improve the performance of traditional geographic data display methods in data indexing, data compression, and the projection of different granularities. First, we use the Geohash encoding system to represent coordinates, as well as to partition and index large-scale geographic data. The data compression and tile encoding is accomplished by Geohash. Second, to realize a direct conversion between Geohash and screen-pixel coordinates, we adopt the relative position projection method. Finally, we improve the calculation and rendering efficiency by using the intermediate result caching method. To evaluate the GeohashTile method, we have implemented the client and the server of the GeohashTile system, which is also evaluated in a real-world environment. The results show that Geohash encoding can accurately represent latitude and longitude coordinates in vector maps, while the GeohashTile framework has obvious advantages when requesting data volume and average load time compared to the state-of-the-art GeoTile system.

Design of Dual-channel HD Audio and Video Recording and Playback Equipment Based on TMS320DM8168

In order to meet the needs of dual-channel high-resolution audio and video recording, storage, and playback, as well as important control scenarios such as shipboard, vehicle, and airborne, the need for long-term capture and recording of terminal screen information is convenient for post-event technical analysis, Service quality assessment, exercise deduction, and operation accident responsibility identification. Researched the high-performance DaVinci series chip TMS320DM8168 launched by TI, designed and implemented a record playback device that supports dual-channel high-definition video, using the compression algorithm of the H.264 encoding standard to achieve compression and storage of video data, Use advanced and mature technology, follow the generalized, modular and serial design ideas to ensure better applicability and higher reliability. After the design was completed, the functions of video capture, compression encoding and video playback were verified, and the performance and stability were tested. The delay of video capture and display is within 40ms, The equipment can support high-definition single-channel or dual-channel video input, and realize two channels video information is compressed and stored, while the video decompression is stable, the playback picture is clear, and there is no jitter. The system functions and performance reach the expected goals.

A process for the accurate reconstruction of pre-filtered and compressed digital aerial images

The study of compression and decompression methods is crucial for storage and/or transmission of large numbers of image data which is required for archiving aerial photographs, satellite images and digital ortho-photos. Hence, the proposed work aims to increment the compression ratio (CR) of digital images in general. While emphasis is made on aerial images, the same principle may find applications to other types of raster based images. The process described here involves the application of pre-defined low-pass filters (i.e. kernels) prior to applying standard image compression encoders. Low-pass filters have the effect of increasing the dependence between neighbouring pixels which can be used to improve the CR. However, for this pre-filtering process to be considered as a compression instrument, it should allow for the original image to be accurately restored from its filtered counterpart.The development of the restoration process presented in this study is based on the theory of least squares and assumes the knowledge of the filtered image and the low-pass filter applied to the original image. The process is a variant of a super-resolution algorithm previously described, but its application and adaptation to the filtering and restoration of images, in this case (but not exclusively) aerial imagery, using a number of scales and filter dimensions is the expansion detailed here. An example of the proposed process is detailed in the ensuing sections. The example is also indicative of the degree of accuracy that can be attained upon applying this process to gray-scale images of different entropies and coded in a lossy or lossless mode.

An effective image compression–encryption scheme based on compressive sensing (CS) and game of life (GOL)

At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and diffusion is utilized. Firstly, a plaintext-dependent game-of-life-based scrambling method is presented to shuffle the sparse coefficient matrix of plain image, and the permutation matrix is constructed by rules of GOL, which may effectively reduce the adjacent pixel correlation and enhance the scrambling effect. Secondly, the confused matrix is compressed by CS and diffused using a key matrix to get the cipher image. Additionally, a five-dimensional (5D) memristive hyperchaotic system is used to generate chaotic sequences. They are utilized to construct measurement matrix, to generate initial cell matrix of GOL and to produce key matrix. Information entropy of plain image and external key parameters are combined to compute initial values of the hyperchaotic system. Therefore, our algorithm has high sensitivity to original image and it may resist against known-plaintext attack and chosen-plaintext attack. Experimental results and performance analyses demonstrate that the proposed encryption algorithm is effective to withstand various typical attacks, and it may be applied for image secure communication.

Novel image compression–encryption hybrid scheme based on DNA encoding and compressive sensing

A novel and effective image compression–encryption scheme based on deoxyribonucleic acid (DNA)-encoding theory and compressed sensing (CS) theory is proposed. The logistic map is applied to control key image and measurement matrices, and to control DNA encoding and decoding rules used to encode and decode each row of the plain image and the key image. The plain image first forms a disordered image through DNA encryption stage, sorts it to obtain an ordered image through sorting stage, and then passes the ordered image through CS encryption stage to form a cipher image. Simulation results and attack analysis verify the validity and feasibility of the encryption scheme, whose compression and security is acceptable.

A Memristor-Based Compressive Sampling Encoder with Dynamic Rate Control for Low-Power Video Streaming

Image sensors are widely used in various applications. With the increasing requirement for high resolutions and frame rates, power consumption has become a critical issue, which limits the use of image sensors in mobile devices and IoT applications. Compressive sensing (CS) techniques can achieve a sub-Nyquist sampling rate to reduce the power consumption in hardware circuits. Currently, most compressive measurements are implemented in digital CMOS circuits, leading to high hardware complexity and power consumption, as well as the limited sampling speed. Furthermore, CS applications with large image sizes are usually based on block-wise methods, which require real-time rate controls during practical operations. In this article, we propose a memristor-based CS encoder that can be integrated with conventional image sensors to achieve high performance with low power consumption and hardware overheads. A self-adaptive compressing rate control mechanism is also devised to maximize the performance of the proposed technique. Simulation results of wireless video streaming demonstrate the advantages of the proposed technique.

Breath-hold and free-breathing quantitative assessment of biventricular volume and function using compressed SENSE: a clinical validation in children and young adults

BackgroundAlthough the breath-hold cine balanced steady state free precession (bSSFP) imaging is well established for assessment of biventricular volumes and function, shorter breath-hold times or no breath-holds are beneficial in children and severely ill or sedated patients.MethodsClinical cardiovascular magnetic resonance (CMR) examinations from September 2019 to October 2019 that included breath-hold (BH) and free-breathing (FB) cine bSSFP imaging accelerated using compressed sensitivity encoding (C-SENSE) factor of 3 in addition to the clinical standard BH cine bSSFP imaging using SENSE factor of 2 were analyzed retrospectively. Patients with structurally normal hearts who could perform consistent BHs were included. Aortic flow measured by phase contrast acquisition was used as a reference for the left ventricular (LV) stroke volume. Comparative analysis was performed for evaluation of biventricular volumes and function, imaging times, quantitative image quality, and qualitative image scoring.ResultsThere were 26 patients who underwent all three cine scans during the study period (16.7 ± 6.4 years, body surface area (BSA) 1.6 ± 0.4 m2, heart rate 83 ± 7 beats/min). BH durations of 8 ± 1 s with C-SENSE = 3 were significantly shorter (p < 0.001) by 33% compared to 12 ± 1 s with SENSE = 2. Actual scan time for BH SENSE (4.9 ± 1.2 min) was comparable to that with FB C-SENSE (5.2 ± 1.5 min; p= NS). Biventricular stroke volume and ejection fraction, and LV mass computed using all three sequences were comparable. There was a small but statistically significant (p < 0.05) difference in LV end-diastolic volume (− 3.0 ± 6.8 ml) between BH SENSE and FB C-SENSE. There was a small but statistically significant (p < 0.005) difference in end-diastolic LV (− 5.0 ± 7.7 ml) and RV (− 6.0 ± 8.5 ml) volume and end-systolic LV (− 3.2 ± 4.3 ml) and RV(− 4.2 ± 6.8 ml) volumes between BH C-SENSE and FB C-SENSE. The LV stroke volumes from all three sequences had excellent correlations (r = 0.96, slope = 0.98–1.02) with aortic flow, with overestimation by 2.7 (5%) to 4.6 (8%) ml/beat. The image quality score was Excellent (16 of 26) to Good (10 of 26) with BH SENSE, Excellent (13 of 26) to Good (13 of 26) with BH C-SENSE, and Excellent (3 of 26) to Good (21 of 26) to Adequate (2 of 26) with FB C-SENSE.ConclusionsImage quality and ventricular volumetric and functional indices using either BH or FB C-SENSE cine bSSFP imaging were comparable to standard BH SENSE cine bSSFP imaging while maintaining nominally identical spatio-temporal resolution. This accelerated image acquisition provides an alternative to accommodate patients with impaired BH capacity.

General image encryption algorithm based on deep learning compressed sensing and compound chaotic system

Many image compression and encryption algorithms based on traditional compressed sensing and chaotic systems are time-consuming, have low reconstruction quality, and are suitable only for grayscale images. In this paper, we propose a general image compression encryption algorithm based on a deep learning compressed sensing and compound chaotic system, which is suitable for grayscale images and RGB format color images. Color images can be directly compressed and encrypted, but grayscale images need copying from 1 channel to 3 channels. First, the original image is divided into multiple 3 × 33 × 33 non-overlapping image blocks and the bilinear interpolation Bilinear and convolutional neural network are used to compress the image, so that the compression network has no restriction on the sampling rate and can obtain high-quality compression of image. Then a composite chaotic system composed of a two-dimensional cloud model and Logistic is used to encrypt and decrypt the compressed image (sliding scrambling and vector decomposition), and finally the decrypted image is reconstructed. In the reconstruction network, the convolutional neural network and bilinear interpolation Bilinear are mainly responsible for reconstructing the contour structure information, and the fully connected layer is mainly responsible for reconstructing and combining the color information to reconstruct a high-quality image. For grayscale images, we also need to calculate the average value of the corresponding positions of the 3 channels of the reconstructed image, and change the 3 channels into 1 channel. The experimental results show that the general image encryption algorithm based on deep learning compressed sensing and compound chaos system has great advantages in data processing quality and computational complexity. Although in the network the color images are used for training, the quality of grayscale image reconstruction is still better than that of other algorithms. The image encryption algorithm has a large enough key space and associates the plaintext hash value with the key, which realizes the encryption effect of one image corresponding to one key, thus being able to effectively resist brute force attacks and selective plaintext attacks. Compared with it in the comparison literature, the correlation coefficient is close to an ideal value, and the information entropy and the clear text sensitivity index are also within a critical range, which enhances the confidentiality of the image.

Combined coding of bit planes of images

The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.

Standard compliant light field lenslet image coding model using enhanced screen content coding framework

With the recent advances in high computing capabilities and reliable delivery networks, it is realistic to expect emerging immersive media applications such as virtual reality (VR) and 360-deg video. ISO/IEC Motion Picture Experts Group (MPEG) has launched an MPEG-I project that focuses on not just the current but also the next-generation immersive media applications. Light field (LF) is one of the candidates under consideration by MPEG-I. A LF lenslet image contains plentiful repeating patterns since each microlens captures the same scene. On the other hand, intrablock copy (IBC) in screen content coding (SCC) increases the coding efficiency of screen content (SC) by finding the repeating patterns. SCC can be an efficient encoder for LF image compression. However, SCC is not optimized for compressing lenslet images. Therefore, we propose an efficient lenslet image coding (LIC) model using SCC to encode lenslet images based on the lenslet image characteristics. Experimental results show that our LIC model further improves the coding efficiency of SCC on lenslet images by 3.72% on average and up to 7.77% in bitrate reduction. For fast approach, LIC is faster than SCC by 44.12%. With our proposed LIC model, the encoded bitstreams are compliant with the SCC standard with an even faster decoding time.

Multiscale transform-based secured joint efficient medical image compression-encryption using symmetric key cryptography and ebcot encoding technique

Due to the huge advancement in technology, digitizing the multimedia content like text, images and videos has become easier. Everyday huge amounts of multimedia content are shared through the social networks using internet. Sometimes this multimedia content can be hacked by the hackers. This will lead to the misuse of the data. On the other hand, the medical content needs high security and privacy. Motivated by this, joint secured medical image compression–encryption mechanisms are proposed in this paper using multiscale transforms and symmetric key encryption techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform and curvelet transform. The encryption techniques involved in this paper are international data encryption algorithm (IDEA), Rivest Cipher (RC5) and Blowfish. The encoding technique used in this paper is embedded block coding with truncation (EBCOT). Experimental results are done for the proposed works and evaluated by using various parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Quality Index (IQI) and Structural Similarity Index (SSIM), Average Difference (AD), Normalized Cross-correlation (NK), Structural Content (SC), Maximum difference (MD), Laplacian Mean Squared Error (LMSE) and Normalized Absolute Error (NAE). It is justified that the proposed approaches in this paper yield good results.

Data Hiding and Compression Encryption through Discrete Wavelet Transform and Block Coding

Data Hiding and Compression Encryption through Discrete Wavelet Transform and Block Coding

High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique

To retrospectively compare sensitivity encoding (SENSE) and compressed sensing-sensitivity encoding (CS-SENSE) for high resolution (HR) cranial nerve magnetic resonance imaging (MRI) in a clinical population. Twenty consecutive patients who were clinically suspected of neurovascular compression syndrome (NVCS) were enrolled in this study. HR three-dimensional isotropic T2-weighted fast spin-echo (T2 VISTA) sequences with SENSE or CS-SENSE, and contrast-enhanced three-dimensional T1-turbo field-echo (CE 3D T1 TFE) with SENSE or CS-SENSE, were compared using quantitative and qualitative methods by two board-certified neuroradiologists. For the T2 VISTA, CS-SENSE was significantly superior to SENSE in terms of cerebrospinal fluid homogeneity. For CE 3D T1 TFE, CS-SENSE was significantly superior to SENSE in terms of the existence of ghost artefact and the signal-to-noise ratio (SNR) of the pontine parenchyma. There was no significant difference in overall image quality between the two techniques. Compared with SENSE, CS-SENSE reduced the scan time to 44.2% of that with SENSE on T2 VISTA, and to 66.1% of that with SENSE of the CE 3D T1 TFE, with the differences being statistically significant (p<0.01, both). For T2 VISTA and CE 3D T1 TFE imaging of patients with suspected NVCS, CS-SENSE appears to offer superior reductions in motion artefact and scan time relative to SENSE, without a loss of overall image quality.

Analysis of Magnetic Resonance Signals from Diffusion Weighted Imaging using Compressed Sensitivity Encoding Technique

The purpose of this study was to compare image quality between compressed sensitivity encoding (CS) and sensitivity encoding (SENSE) in single-shot turbo spin echo diffusion-weighted imaging (TSE-DWI). Signal-tonoise ratios (SNR), apparent diffusion coefficient (ADC) values, and geometric accuracies were measured with an American College of Radiology head phantom. CS showed a high mean SNR, less variation in ADC values, and reduced imaging acquisition time (21.7 %-39.3 %) compared with SENSE. In addition, there was no significant difference in geometric accuracy between SENSE and CS. In conclusion, in comparison with SENSE TSE-DWI, CS TSE-DWI resulted in a reduced imaging acquisition time and improved SNR, while maintaining image quality.

Design and Optimization for Distributed Compress-and-Forward System based on Multi-Relay Network

A novel distributed compress-and-forward (CF) system based on multi-relay network is presented. In this system, as the direct link between the source and destination is invalid due to some reasons, such as the limited power, special working environment, or even economic factors, relays are employed to receive analog signals and carry on distributed compressed encoding. Subsequently, the digital signals are transmitted to the destination via wireless channel. Moreover, a theoretical analysis for the system is provided by utilizing the Chief Executive Officer (CEO) theory and Shannon channel capacity theory, and the rate-distortion function as well as the connection between the transmission rate and the channel capacity are constructed. In addition, an optimal signal-to-noise ratio (SNR) -based power allocation method is proposed to maximize the quantization SNR under the limited total power. Simulation result shows that the proposed CF system outperforms the amplify-and-forward (AF) system versus the SNR performance.

DNA sequence compression using RP/GP2 method with information storage and security

In the field of bioinformatics, the storing and transmission of deoxyribonucleic acid (DNA) is essential for compression rate, ratio and encryption point of view. The DNA sequence requires huge space for storing and time for encryption causing considerable delay transmission of information. The most of the users demand low storage and computational cost, therefore efficient algorithm is required for compression. Proposed algorithm will be based on combinations of reverse and palindrome (RP) technique or genetic palindrome and palindrome (GP2) technique substring substitution. The variable substring will be replaced by the corresponding American Standard Code for Information Interchange (ASCII) code which is extracted RP/GP2. The transmission of DNA/RNA/Protein sequence over wireless communication is common issue. The size of DNA sequence is growing twice in a year proportionally. As a result, security of these data from unauthorized user is essential. The proposed selection encryption reduces the computational recourses for this data. The DNA sequence security is ensured by signature which depends on ASCII code and dynamic library file acting as a key. This approach shows that 95% of original file is modified when 44–45% is encrypted. The experimental results shows that the compression rate is 3.7750 bits/base.

Combined feature compression encoding in image retrieval

Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral regression (SR) is applied in this paper. We combine SR and the robust representation of the CNN to form a combined feature compression encoding (CFCE) method. CFCE preserve the information without noticeably impacting image retrieval accuracy. We find the tendency of the image retrieval performance to change the compressed dimensionality of features. We further discover a reasonable dimensionality of indexing in image retrieval. Experiments demonstrate that our model provides state-of-the-art performances across datasets.

New Time-Domain Approach for Digital Signal Processing: A Set of Experimental Measures for Systems with High Transmission Rates

The essential purpose of this paper is to introduce to the current literature a new theoretical approach on how to implement the process of encoding data using a unique treatment with respect to the digital signal processing. Some relationships between a set of digital pulses and a special class of real polynomials allow us to identify the generation of a single pulse with an intrinsic dependence on the zero values of an arbitrary equation represented by a polynomial equation, where this direct match is established through an analytic operator. By using an ordinary field-programmable gate array architecture, it is possible to validate our theoretical approach, and we are presenting some experimental measurements, as well as one application on how to build a commercial data compressor for fiber optics. The algorithm here introduced presents an innovative technique, and its performance is faster per comparison. Furthermore, thanks to the math-to-time-domain transformations, it tends to overcome the current time required to process polynomial equations, which are involved in the data compression and data encryption systems.

A modified image selective encryption-compression technique based on 3D chaotic maps and arithmetic coding

The advances in digital image processing and communications have created a great demand for real–time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression encryption systems are still a research problem as the intrinsic features of images such as bulk data capacity and high correlation among pixels hinds the use of the traditional joint encryption compression methods. A new approach is suggested in this paper for partial image encryption compression that adopts chaotic 3D cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilized as a lossy compression technique instead of using complex quantization techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognizable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique. The compression analysis and security outcomes indicate that the suggested technique is an efficacious and safe for real time image’s applications.

Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.