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Medical image encryption scheme based on self-verification matrix

IET image PROCESSING 2021年第12期15卷 2787-2798页

作者： Man, Zhenlong Li, Jinqing Di, Xiaoqiang CUST Sch Comp Sci & Technol Changchun 130022 Peoples R China CUST Prov Key Lab Network & Informat Secur Changchun 130022 Peoples R China CUST Informat Ctr Changchun 130022 Jilin Peoples R China

To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti-tampering methods and security, this report presents an anti-tampering encryption algorithm for medical images that is based on a self-verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two-dimensional matrix (a self-verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self-verification matrix. To improve the security of the self-authentication matrix, DNA coding is also applied to the self-authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self-verification matrix are mixed and cross-scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti-tampering capabilities, and can accurately locate at least 4 pixels.

关键词： Medical and biomedical uses of fields, radiations, and radioactivity health physics Cryptography image and video coding Biomedical measurement and imaging Computer vision and image processing techniques Data security Patient diagnostic methods and instrumentation Biology and medical computing

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Research on PDMA system based on complementary sequence and low complexity detection algorithm

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IET COMMUNICATIONS 2021年第20期15卷 2586-2596页

作者： Li, Shufeng Su, Baoxin Jin, Libiao Sun, Yao Xia, Zhiping Commun Univ China Sch Informat & Commun Engn State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Univ Glasgow James Wart Sch Engn Glasgow Lanark Scotland Acad Broadcasting Sci Beijing Peoples R China

With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non-orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC-PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max-log MPA (SSCM-MPA) algorithm is proposed to reduce the complexity of the multi-user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal-to-noise ratio (PSNR) while reducing bit error rate (BER). SSCM-MPA algorithm can greatly reduce the complexity and improve the transmission efficiency.

关键词： Electromagnetic compatibility and interference image and video coding Signal detection Communication switching Multiple access communication Multimedia communications Radio links and equipment video signal processing Multimedia Error statistics (inc. error probability) Error statistics (inc. error probability)

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Policing method for Markovian traffic descriptors of VBR MPEG sources over ATM networks

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IEE PROCEEDINGS-COMMUNICATIONS 2001年第2期148卷 70-76页

作者： Yoo, SJ Kim, SD Inha Univ Grad Sch Informat Technol & Telecommun Inchon 402751 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

An efficient policing mechanism for Markov model-based traffic descriptors of VBR MPEG video traffic is proposed. A VBR video sequence is described by a set of traffic descriptors using a scene-based Markov model for effective resource allocation and accurate QoS prediction. To monitor the input traffic from the source, the authors have defined a set of a limited number of representative monitoring parameters and also defined an exponential bounding function in order to accommodate dynamic behaviour. The simulation results show that the proposed policing mechanism for Markovian traffic descriptors monitors sophisticated traffic, such as MPEG video, effectively and also protects network resources from both malicious and abnormally behaved traffic.

关键词： code standards variable rate codes exponential bounding function quality of service video coding telecommunication standards telecommunication congestion control network resource allocation management monitoring parameters ATM networks bandwidth utilisation Communication switching call admission control input traffic monitoring telecommunication network management Markov processes network resources protection malicious traffic QoS prediction policing method packet switching video sequence scene-based Markov model telecommunication traffic Markovian traffic descriptor visual communication VBR MPEG sources abnormally behaved traffic image and video coding Network management flow control simulation results asynchronous transfer mode dynamic behaviour MPEG video traffic image sequences resource allocation

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Reversible data hiding for encrypted image based on adaptive prediction error coding

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IET image PROCESSING 2021年第11期15卷 2643-2655页

作者： Tang, Zhenjun Pang, Mingyuan Yu, Chunqiang Fan, Guijin Zhang, Xianquan Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Normal Univ Dept Comp Sci 15 Yu Cai Rd Guilin 541004 Peoples R China

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.

关键词： Codes Cryptography image and video coding Computer vision and image processing techniques Data security Spatial and pictorial databases

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Classified image compression using optimally structured auto-association networks

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IET image PROCESSING 2007年第2期1卷 189-196页

作者： Abbas, H. M. Mentor Graphics Corp Cairo 11341 Egypt

Here, an application of a set of auto-association networks with linear output neurons and sigmoidal hidden neurons for classified image compression is carried out. Simulations and statistical analysis of this type of network have shown that, at convergence, the hidden neurons operate mainly in their linear region. The nearly linear behaviour of the hidden neurons is exploited in finding out the minimum number of hidden neurons needed to reconstruct image data within a certain error threshold. Four optimally structured auto-association networks are set up so that each network is trained to encode a certain variance-based class of image blocks. Results have shown excellent performance of the proposed architecture in reproducing high-quality images at a low bit rate.

关键词： sigmoidal hidden neurons optimally structured auto-association networks linear output neurons statistical analysis Computer vision and image processing techniques image classification image and video coding image coding image data reconstruction image blocks classified image compression Other topics in statistics image reconstruction

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Real-time video object segmentation in H.264 compressed domain

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IET image PROCESSING 2009年第5期3卷 272-285页

作者： Mak, C. -M. Cham, W. -K. Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

In this study the authors proposed a real-time video object segmentation algorithm that works in the H.264 compressed domain. The algorithm utilises the motion information from the H.264 compressed bit stream to identify background motion model and moving objects. In order to preserve spatial and temporal continuity of objects, Markov random field (MRF) is used to model the foreground field. Quantised transform coefficients of the residual frame are also used to improve segmentation result. Experimental results show that the proposed algorithm can effectively extract moving objects from different kinds of sequences. The computation time of the segmentation process is merely about 16 ms per frame for CIF size frame, allowing the algorithm to be applied in real-time applications.

关键词： residual frame background motion model motion information Computer vision and image processing techniques real-time video object segmentation image and video coding Markov processes image coding moving objects Markov random field spatial continuity temporal continuity H.264 compressed bit stream quantised transform coefficients data compression H.264 compressed domain image segmentation

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Innovative technologies for space-based radars

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2003年第3期150卷 104-112页

作者： Zahn, R Astrium GmbH D-88039 Friedrichshafen Germany

In recent years technology developments for SAR front-end and for on-board SAR data processing have been performed at Astrium GmbH, Germany. Remarkable progress has been made in the respective demonstrator projects DE... 详细信息

关键词： TOPAS demonstrator projects Astrium SAR image compression data compression research initiatives SAR front-end Radar theory on-board SAR data processing Research and development space-based radar Radar equipment, systems and applications image coding EUCLID RTP 9.7 DESA radar imaging image and video coding Germany synthetic aperture radar innovative technologies

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Enhancing the predictive coding efficiency with control technologies for lossless compression of images

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IET image PROCESSING 2012年第3期6卷 251-263页

作者： Lee, C. -H. Kau, L. -J. Natl Chung Hsin Univ Dept Mech Engn Taichung 402 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Grad Inst Comp & Commun Engn Taipei 10608 Taiwan

This study applies techniques commonly used in control systems to enhance the efficiency of predictive coding in lossless compression of images for pixels around boundaries. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be regarded as the system model. Besides, the prediction error is usually feedback for the adaptation of predictor coefficients so that the prediction error of consecutive pixels can be minimised. When compared with a control system, which is to follow the system command as precisely as possible, the authors find the objective of both systems are the same. Moreover, a boundary among image pixels can be considered a step command in control systems. These observations lead to the idea of using control technologies to improve the prediction result around boundaries. To realise this idea, an adaptive Takagi-Sugeno fuzzy neural network and a proportional controller in control systems are applied as the predictor and the error compensator, respectively. To accelerate the run-time performance of the proposed system under limited resources, the online training area is even not used for network adaptation, but the performance is still comparable with state-of-the-art predictors and coders as the authors will see in the experiment.

关键词： adaptive Takagi-Sugeno fuzzy neural network predictor Neural computing techniques error compensator control technology Computer vision and image processing techniques image and video coding proportional control image coding multiinput single-output system predictive coding lossless image compression proportional controller neural nets

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YOLOFig detection model development using deep learning

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IET image PROCESSING 2021年第13期15卷 3071-3079页

作者： Lawal, Olarewaju Mubashiru Zhao, Huamin Shanxi Agr Univ Coll Agr Engn Jinzhong 030801 Peoples R China

The detection of fruit, including accuracy and speed is of great significance for robotic harvesting. Nevertheless, attributes such as illumination variation, occlusion, and so on have made fruit detection a challenging task. A robust YOLOFig detection model was proposed to solve detection challenges and to improve detection accuracy and speed. The YOLOFig detection model incorporated Leaky activated ResNet43 backbone with a new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling network (SPPNet), feature pyramid network (FPN), complete (CIoU) loss, and distance DIoU-NMS to improve the fruit detection performance. The obtained average precision (AP) and speed (frames per second or fps) respectively under 2,3,4,3,2 residual block arranged backbone for YOLOv3b is 78.6% and 69.8 fps, YOLOv4b is 87.6% and 57.1 fps, and YOLOFig is 89.3% and 96.8 fps;under 1,2,8,8,4 residual block arranged backbone for YOLOv3 is 77.1% and 56.3 fps, YOLOv4 is 87.1% and 52.5 fps, and YOLOResNet70 is 87.3% and 79 fps;and under 3,4,6,3 residual block arranged backbone for YOLOResNet50 is 85.4% and 77.1 fps. An indication that the new residual block arranged backbone of 2,3,4,3,2 outperformed 1,2,8,8,4 on an average AP of 1.33% and detection speed of 15.2%. Finally, the compared results showed that the YOLOFig detection model performed better than other models at the same level of residual block arrangement. It can better generalize and is highly suitable for real-time harvesting robots.

关键词： Optical, image and video signal processing image and video coding Computer vision and image processing techniques

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Dynamic convolutional capsule network for In-loop filtering in HEVC video codec

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IET image PROCESSING 2023年第2期17卷 439-449页

作者： Su, LiChao Cao, Mengqing Yu, Yue Chen, Jian Yang, XiuZhi Wu, Dapeng Fuzhou Univ Coll Software Coll Comp & Data Sci Fuzhou Fujian Peoples R China Fuzhou Univ Coll Phys & Informat Engn Qi Shan Campus2 Xue Yuan Rd Fuzhou Fujian Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Recently, several in-loop filtering algorithms based on convolutional neural network (CNN) have been proposed to improve the efficiency of HEVC (High Efficiency video coding). Conventional CNN-based filters only apply a single model to the whole image, which cannot adapt well to all local features from the image. To solve this problem, an in-loop filtering algorithm based on a dynamic convolutional capsule network (DCC-net) is proposed, which embeds localized dynamic routing and dynamic segmentation algorithms into capsule network, and integrate them into the HEVC hybrid video coding framework as a new in-loop filter. The proposed method brings average 7.9% and 5.9% BD-BR reductions under all intra (AI) and random access (RA) configurations, respectively, as well as, 0.4 dB and 0.2 dB BD-PSNR gains, respectively. In addition, the proposed algorithm has an outstanding performance in terms of time efficiency.

关键词： Codecs, coders and decoders HEVC hybrid video coding framework convolutional neural network image and video coding dynamic convolutional capsule network CNN-based filters Neural nets video coding HEVC video codec video codecs dynamic routing in-loop filtering algorithm video signal processing dynamic segmentation algorithms high efficiency video coding convolutional neural nets

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