Compression Encryption Research Articles

Echo planar imaging with compressed sensitivity encoding (EPICS): Usefulness for head and neck diffusion-weighted MRI

PurposeTo evaluate diffusion-weighted imaging (DWI) using echo planar imaging (EPI) with compressed SENSE (EPICS) of the head and neck magnetic resonance imaging (MRI). MethodWe retrospectively observed 32 patients who underwent head and neck DWI according to either the conventional method (SENSE, reduction factor = 2), fast scanning method (SENSE, reduction factor = 4), or fast scanning method with EPICS (EPICS, reduction factor = 4). For quantitative analysis, contrast-to-noise-ratio (CNR), apparent diffusion coefficient (ADC) values, geometric distortion, and coefficient of variations (CV) were measured and compared. For qualitative analysis, all images were independently and blindly evaluated by two board-certified radiologists. ResultsEPICS revealed the higher CNR between all location compared to those of SENSE with reduction factor = 4. Distortion in the anterior-posterior direction was significantly lower on EPICS than on the conventional scan (p = 0.02). A comparison between the ADC values of the EPICS and conventional scan revealed no significant differences. The CV was significantly lower for EPICS than the conventional scan [DWI: 0.22 (IQR: 0.15–0.30) vs 0.32 (IQR: 0.24–0.40), p = 0.02]. ConclusionsCompressed SENSE combined with the high acceleration factor can improve image quality, homogeneity, and distortion in the head and neck DWI maintaining ADC values and the scan time duration.

A 1Mb Mixed-Precision Quantized Encoder for Image Classification and Patch-Based Compression

Even if Application-Specific Integrated Circuits (ASIC) have proven to be a relevant choice for integrating inference at the edge, they are often limited in terms of applicability. In this paper, we demonstrate that an ASIC neural network accelerator dedicated to image processing can be applied to multiple tasks of different levels: image classification and compression, while requiring a very limited hardware. The key component is a reconfigurable, mixed-precision (3b/2b/1b) encoder that takes advantage of proper weight and activation quantizations combined with convolutional layer structural pruning to lower hardware-related constraints (memory and computing). We introduce an automatic adaptation of linear symmetric quantizer scaling factors to perform quantized levels equalization, aiming at stabilizing quinary and ternary weights training. In addition, a proposed layer-shared Bit-Shift Normalization significantly simplifies the implementation of the hardware-expensive Batch Normalization. For a specific configuration in which the encoder design only requires 1Mb, the classification accuracy reaches 87.5% on CIFAR-10. Besides, we also show that this quantized encoder can be used to compress image patch-by-patch while the reconstruction can performed remotely, by a dedicated full-frame decoder. This solution typically enables an end-to-end compression almost without any block artifacts, outperforming patch-based state-of-the-art techniques employing a patch-constant bitrate.

A Lossless Compression and Encryption Method for Remote Sensing Image Using LWT, Rubik’s Cube and 2D-CCM

This paper proposes a lossless encryption–compression algorithm for large-scale remote sensing images. Firstly, the red, green and blue components of color image are compressed by a lossless predictive encoding. Then, the lifting wavelet transform (LWT) is used to decompose the encoding results. And a new Rubik’s cube transformation is introduced to scramble the decomposed coefficients, which uses the chaotic sequence generated by 2D Cubic–Chebyshev map (2D-CCM). The initial values of 2D-CCM are obtained from the chi-square test values of the three components, which leads to the algorithm related to the plaintext image. After that, the scrambling coefficients are thresholding, and the position sequences generated in the process are encrypted and compressed by the proposed encrypted run-length encoding (E-RLE). The processed coefficients are further compressed by Huffman encoding. At the end, the final results are obtained by a novel helix diffusion which is related to the chaotic sequence. Experimental results show that, this algorithm achieves higher lossless compression ratio with lower time complexity, and the encryption scheme has higher security.

DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments

Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We enhance classical ICP-based registration with neural feature-based matching to reliably find point correspondences. Our novel 3D convolutional and attention-based network is trained in an end-to-end fashion to learn features, which are well suited for matching and for rating the quality of the point correspondences. By utilizing a compression encoder, we can directly operate on a compressed map representation, making our approach well suited for operation under memory constraints. We evaluate our approach on point clouds obtained at completely different points in time, showing that our approach is able to register point clouds even under those challenging conditions reliably.

Construction of Wireless Sensor Network Video Surveillance System for Multimedia Classroom Education and Teaching under 5G Communication Network

Research on wireless sensor network video surveillance system is a hot field of media classroom education and teaching. Based on the 5G communication network theory, this paper constructs a wireless sensor network video monitoring system for multimedia classroom education and teaching, systematically analyzes the new distance education model under the condition of the integration of the two networks, and improves the interactive live classroom education model. For the classroom teaching mode, the model studies the isochronous transmission technology of data collection and video encoding and decoding, adopts hardware compression encoding, integrates key technologies such as reliable multicast and conditional access, and proposes the design of the system scheme, which solves the problem of multimedia classroom education and teaching digital issues. During the simulation process, the MATLAB software platform was used to study the effect of the number of 5G communication network nodes and node attributes (such as node position, node perception angle, instantaneous perception direction, or direction difference between neighboring nodes, node speed, etc.) on the target area or target point. The experimental results show that the performance of the joint optimization of transmission video quality and network lifetime performs well under multipath conditions. The total utility under multipath finally converges to 8.3, while the single path finally converges to 6.5, which further promotes the real-time performance of the multimedia network classroom teaching system.

Multi-Image Compression-Encryption Algorithm Based on Compressed Sensing and Optical Encryption.

In order to achieve large-capacity, fast and secure image transmission, a multi-image compression–encryption algorithm based on two-dimensional compressed sensing (2D CS) and optical encryption is proposed in this paper. Firstly, the paper uses compressed sensing to compress and encrypt multiple images simultaneously, and design a new structured measurement matrix. Subsequently, double random phase encoding based on the multi-parameter fractional quaternion Fourier transform is used to encrypt the multiple images for secondary encryption, which improves the security performance of the images. Moreover, a fractional-order chaotic system with more complex chaotic behavior is constructed for image compression and encryption. Experimental results show that the algorithm has strong robustness and security.

General Image Fast Encryption Algorithm for Deep Learning Compression and Reconstruction

Aiming at the problems of long time-consuming in image compression encryption and color distortion of the decrypted image, a fast and safe general image encryption algorithm is proposed. The compression network uses bilinear interpolation and a fully connected layer for compression preprocessing to prevent the loss of color information. Convolution layer improves compression rate and reduces time complexity in this algorithm. In the encryption part, the chaotic sequence dynamically selects the pixel block and the pixel points in the block combining the XOR operation to perform pixel diffusion, which can improve the randomness of the algorithm. In the improved bilinear convolution neural network, bilinear and convolutional layers are used to obtain the image contour features. And the fully connected layer obtains color information for image reconstruction. This algorithm realizes the integration of compression, encryption, decryption and restoration of grayscale and RGB format color images. The experimental results show that the various encryption indexes of this algorithm are close to ideal values. It has good encryption security and anti-attack ability. Moreover, the encryption time is greatly shortened. On the 500 test set (BSD500) commonly used in image processing, when the sampling rate is 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio can reach to 29.01 dB, 25.69 dB, 23.75 dB and 21.45 dB, which is better than FCLBCNN. This encryption algorithm effectively solves the color distortion problem of reconstructed images and has better encryption effects and wider applicability.

An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher.

In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption. The parallel compressed sensing method is adopted to ensure the encryption and decryption efficiency. The ZUC stream cipher is used to sample the one-dimensional chaotic map to reduce the correlation between elements and improve the randomness of the chaotic sequence. The compressed sensing measurement matrix is constructed by using the sampled chaotic sequence to improve the image restoration effect. In order to reduce the block effect after the parallel compressed sensing operation, we also propose a method of a random block of images. Simulation analysis shows that the algorithm demonstrated better encryption and compression performance.

RETRACTED ARTICLE: An effective text compression–encryption using tight and indirect encryptions

RETRACTED ARTICLE: An effective text compression–encryption using tight and indirect encryptions

Entropy encoder for low-power low-resources high-quality CFA image compression

An entropy encoder for high-quality image compression in low-power, low-resources devices like wireless capsule endoscopy (WCE) or wireless camera sensor network (WCSN) is proposed. The proposed entropy encoder is optimized for encoding data resulting from transformation and quantization of images captured with a color filter array (CFA) digital camera. It efficiently exploits existing correlations among color components in CFA images to improve the performance of the image compressor in terms of power consumption, silicon area, and compression ratio. The image compressor based on the new encoder requires only 22% of the memory size needed by the JPEG-LS-based CFA compressor (for an image with 512 columns). Despite this significant reduction in memory size, the average compression ratio in relation to the image quality is superior to other state-of-the-art algorithms of similar computational complexity and memory requirements. The image compressor based on the proposed encoder has been synthesized using a low-cost UMC 180 nm CMOS process as an intellectual property (IP) core. The design requires a very small silicon area (0.54 × 0.50 mm) and achieves excellent energy efficiency (30μJ per a single 512 × 512 image frame and 20 fps). The selected technology for the implementation is not only low-cost but also well suited to implement an RF transmitter in addition to the compressor to provide a cost effective System on Chip (SoC) for resource-constrained devices like WCE or WCSN.

Image lossless encoding and encryption method of SPECK based on 1D chaotic map

In order to satisfy the requirements of high quality and security during image transmission and storage, this paper proposes an image lossless compression encryption algorithm based on 1D chaotic map and Set Partitioned Embedded block encoder (SPECK). Initially, this paper proposes a new 1D chaotic map, and applies the chaotic sequences generated by it to each stage of the compression encryption algorithm. In addition, according to the feature that the degree of energy concentration in the wavelet coefficient matrix gradually decreases from low frequency to high frequency, this paper proposes a wavelet coefficient encryption algorithm, which can balance security and compression performance. Furthermore, multiple encryption points are introduced in the SPECK encoding process, and a secure SPECK encoding algorithm is proposed. Finally, theoretical analysis and simulation results show that the proposed algorithm is close to the theoretical value in terms of information entropy, correlation coefficient, mean square error of reconstructed image and other related indicators. Therefore, the algorithm has high security and lossless compression performance.

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing.

Many image encryption schemes based on compressive sensing have poor reconstructed image quality when the compression ratio is low, as well as difficulty in hardware implementation. To address these problems, we propose an image encryption algorithm based on the mixed chaotic Bernoulli measurement matrix block compressive sensing. A new chaotic measurement matrix was designed using the Chebyshev map and logistic map; the image was compressed in blocks to obtain the measurement values. Still, using the Chebyshev map and logistic map to generate encrypted sequences, the measurement values were encrypted by no repetitive scrambling as well as a two-way diffusion algorithm based on GF(257) for the measurement value matrix. The security of the encryption system was further improved by generating the Secure Hash Algorithm-256 of the original image to calculate the initial values of the chaotic mappings for the encryption process. The scheme uses two one-dimensional maps and is easier to implement in hardware. Simulation and performance analysis showed that the proposed image compression–encryption scheme can improve the peak signal-to-noise ratio of the reconstructed image with a low compression ratio and has good encryption against various attacks.

High-resolution magnetic resonance imaging of the triangular fibrocartilage complex using compressed sensing sensitivity encoding (SENSE)

PurposeTo evaluate the optimal sequence for high-resolution magnetic resonance imaging (MRI) of the triangular fibrocartilage complex (TFCC) using compressed sensing-sensitivity encoding (CS-SENSE). MethodsThree-dimensional fast field echo T2-weighted images were obtained from 13 healthy volunteers using the original, high spatial resolution sequence with CS-SENSE [HR (CS-SENSE)] and without CS-SENSE (HR) and super-high spatial resolution sequence with CS-SENSE [S-HR (CS-SENSE)] and without CS-SENSE (S-HR). For qualitative analysis, the number of patients affected by motion artifacts in each sequence was counted, and the visualization of the TFCC anatomic structures and overall image quality were categorized. For the quantitative analysis, relative signal intensity (SI) and relative contrast of the lunate bone marrow, lunate cartilage, and disk proper in the wrist joint were all calculated. ResultsThe HR (CS-SENSE) sequence showed better visualization scores than the original sequence in the triangular ligament at the ulnar styloid tip, dorsal radioulnar ligament, and ulnotriquetral ligament. Similarly, the S-HR (CS-SENSE) sequence showed better visualization scores than the original sequence in the triangular ligament at the ulnar styloid tip and dorsal radioulnar ligament. Overall image quality scores were not significantly different, and motion artifacts in the HR and S-HR sequences were observed in 3 of the 13 patients. In contrast, the original sequence showed higher values than those in the HR (CS-SENSE) and S-HR (CS-SENSE) sequences in relative SI of the bone marrow and relative contrast of the cartilage-bone marrow and cartilage-disk proper. ConclusionsOut of the three sequences, the HR (CS-SENSE) sequence provided the highest visualization score and diagnostically sufficient image quality score, although relative SI and relative contrast were low. The HR (CS-SENSE) sequence may be clinically useful for imaging TFCCs.

Performance of C-SENSE Accelerated Rapid Liver Shear Stiffness Measurement Using Displacement Wave Polarity-Inversion Motion Encoding: An Evaluation Study.

Liver shear stiffness measurement using magnetic resonance elastography (MRE) aids in the noninvasive diagnosis and staging of liver fibrosis. Inadequate breath-holds can lead to inaccurate stiffness estimation and/or failed MRE exams. To prospectively evaluate the performance of compressed sensitivity encoding (C-SENSE) accelerated rapid MRE measurement of liver shear stiffness using displacement wave polarity-inversion motion encoding. Retrospective. Eleven with liver disease and 10 asymptomatic subjects. 1.5 T; gradient-recalled-echo (GRE) MRE. All participants underwent: 1) two-dimensional (2D) GRE MRE with inflow saturation using SENSE acceleration factor (R) of 2 (standard of care [SC]); 2) 2D rapid MRE with (RwS); and 3) without (RnS) inflow saturation using C-SENSE R=3; and 4) spatial three-dimensional (3D) rapid MRE with inflow saturation (R3D) using C-SENSE R=4; with nominally identical spatial resolution and coverage. Image analyst (D.G., 2 years of experience) drew identical and maximal regions of interest (ROIs) in right hepatic lobe. Linear regression, intra-class correlation coefficients (ICC), Bland-Altman analyses, and the Wilcoxon signed-rank test were used to assess consistency and agreement of liver stiffness measurements for manually drawn identical and maximal ROIs. In 21 participants (37 ± 14 years) with liver stiffness (2.3 ± 0.7kPa), body mass index (BMI 27 ± 7kg/m2 ), proton density fat fraction (PDFF 9 ± 9%), and T2 * (27 ± 4msec); rapid MRE sequences showed excellent agreement (ICC > 0.95) with SC MRE and no correlation (r2 < 0.1) of the differences (mean difference <0.2kPa, <6%; limits of agreement <0.4kPa, <16%) with BMI, PDFF, and T2 *. Breath-hold times were: 14 seconds (SC), 5seconds (RnS), 7seconds (RwS) per slice, and 16 seconds for the R3D acquisition. C-SENSE accelerated GRE MRE sequences, using displacement wave polarity-inversion motion encoding, produce equivalent measurements of liver stiffness and have potential clinical benefit in patients with limited breath-holding capacity. 1 TECHNICAL EFFICACY: Stage 1.

Compressed SENSE in Pediatric Brain Tumor MR Imaging

PurposeTo compare the image quality, examination time, and total energy release of a standardized pediatric brain tumor magnetic resonance imaging (MRI) protocol performed with and without compressed sensitivity encoding (C-SENSE). Recently introduced as an acceleration technique in MRI, we hypothesized that C‑SENSE would improve image quality, reduce the examination time and radiofrequency-induced energy release compared with conventional examination in a pediatric brain tumor protocol.MethodsThis retrospective study included 22 patients aged 2.33–18.83 years with different brain tumor types who had previously undergone conventional MRI examination and underwent follow-up C‑SENSE examination. Both examinations were conducted with a 3.0-Tesla device and included pre-contrast and post-contrast T1-weighted turbo-field-echo, T2-weighted turbo-spin-echo, and fluid-attenuated inversion recovery sequences. Image quality was assessed in four anatomical regions of interest (tumor area, cerebral cortex, basal ganglia, and posterior fossa) using a 5-point scale. Reader preference between the standard and C‑SENSE images was evaluated. The total examination duration and energy deposit were compared based on scanner log file analysis.ResultsRelative to standard examinations, C‑SENSE examinations were characterized by shorter total examination times (26.1 ± 3.93 vs. 22.18 ± 2.31 min; P = 0.001), reduced total energy deposit (206.0 ± 19.7 vs. 92.3 ± 18.2 J/kg; P < 0.001), and higher image quality (overall P < 0.001).ConclusionC‑SENSE contributes to the improvement of image quality, reduction of scan times and radiofrequency-induced energy release relative to the standard protocol in pediatric brain tumor MRI.

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

Agricultural robots are flexible to obtain ambient information across large areas of farmland. However, it needs to face two major challenges: data compression and filtering noise. To address these challenges, an encoder for ambient data compression, named Tiny-Encoder, was presented to compress and filter raw ambient information, which can be applied to agricultural robots. Tiny-Encoder is based on the operation of convolutions and pooling, and it has a small number of layers and filters. With the aim of evaluating the performance of Tiny-Encoder, different three types of ambient information (including temperature, humidity, and light) were selected to show the performance of compressing raw data and filtering noise. In the task of compressing raw data, Tiny-Encoder obtained higher accuracy (less than the maximum error of sensors ±0.5°C or ±3.5% RH) and more appropriate size (the largest size is 205 KB) than the other two auto-encoders based convolutional operations with different compressed features (including 20, 60, and 200 features). As for filtering noise, Tiny-Encoder has comparable performance with three conventional filtering approaches (including median filtering, Gaussian filtering, and Savitzky-Golay filtering). With large kernel size (i.e., 5), Tiny-Encoder has the best performance among these four filtering approaches: the coefficients of variation with the large kernel (i.e., 5) were 8.6189% (temperature), 10.2684% (humidity), 57.3576% (light), respectively. Overall, Tiny-Encoder can be used for ambient information compression applied to microcontrollers in agricultural information acquisition robots. Keywords: agricultural information, robot, ambient information, data compression, embedded machine learning methods DOI: 10.25165/j.ijabe.20221504.6911 Citation: Wang Y T, Li M Z, Ji R H, Wang M J, Zhang Y, Zheng L H. Novel encoder for ambient data compression applied to microcontrollers in agricultural robots. Int J Agric & Biol Eng, 2022; 15(4): 197–204.

Effective Hardware Accelerator for 2D DCT/IDCT Using Improved Loeffler Architecture

This paper proposes an effective hardware accelerator for 2D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) using an improved Loeffler architecture. The accelerator optimizes the data stream of the Loeffler 8-point 1D DCT/IDCT according to the characteristics of image and video processing. An 8-stage pipeline structure greatly improves the processing speed by reasonably dividing the number of clock cycles and simplifying the arithmetic operations in each cycle. The multiplication-free approximation of the DCT coefficients is implemented through adders and shifters, combined with both fixed-point and canonic signed digit (CSD) coding. In particular, the proposed fast parallel transposed matrix architecture achieves the function of row-column coefficient conversion with lower circuit complexity. The FPGA implementation of the proposed architecture uses a Virtex-7 XC7VX330T device, running at 288 MHz with a throughput of 558 M Pixel/sec, and a Full HD real-time frame rate of up to 269 fps. Only 33 cycles are required to complete the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> blocks of 2D DCT/IDCT, which can be used as a high-performance hardware accelerator for image and video compression encoding.

Development of an efficient real-time H.264/AVC advanced video compression encryption scheme

Multimedia is the combination of media such as text, graphics, video clips, and audio files. In today’s world, multimedia plays an important role in many applications that we use in our daily lives. It is used in educational software, animation, sound, and text, as well as multi-media software. H.264/AVC video compression is extremely efficient in terms of compression. Despite this, H.264/AVC requires a lot of processing and consumes a lot of power insdespite of the fact that its compression efficiency is lower than that of H.264/AVC. We examine the various methods of Video H.264 Advanced Video Compression Standard Encryption Schemes in this paper. The performance of all types of encryption techniques will be evaluated using parameters such as cost overhead, delay, and encryption quality. This will provide us with a detailed comparative analysis of video encryption schemes, allowing us to determine which one is far more efficient for H.264/AVC.

A novel Image Security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

Image compression–encryption scheme using SPIHT and chaotic systems

The world is moving towards digitization for all forms of data. Images are one of the most used data that contains a lot of information. With the increasing network technology, the sharing of data across the internet has also manifold. Certain images are confidential and require security for storing or transmitting on an insecure communication channel. Digital images usually have highly correlated data where compression can be performed to reduce the load on the transmission channel or storage memory. The paper proposes an algorithm to compress the digital images and encryption is performed on the compressed data to provide security. Compression of the image is performed using the Set partitioning in hierarchical trees algorithm. The compressed image is encrypted using a chaotic sequence generated using the Henon map and the cipher image is scrambled using a permutation table obtained from Piecewise Linear chaotic map. To reduce the computation while generating the chaotic sequence, a base conversion operation is used. The initial parameters of the chaotic systems are generated from the elliptic curve point are shared securely using ElGamal based elliptic curve cryptosystem. Security analyses are conducted along with a comparison with other existing systems. The proposed algorithm is secured against various attacks and outperforms existing techniques concerning various statistical and security aspects of an enciphering algorithm.