Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bo...Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.展开更多
With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color image...With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.展开更多
In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compres...In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).展开更多
The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can b...The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.展开更多
This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image compression.Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing ima...This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image compression.Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding.It transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a threshold.PSO is applied for selecting a particle assigned to the threshold values for the subbands.Nine positions assigned to particles values are used to represent population.Every particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe value.The experiments are applied on five different medical image types,i.e.,MRI,CT,and X-ray.Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.展开更多
Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,...Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.展开更多
Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compres...Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by展开更多
be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each i...be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.展开更多
The amount of image data generated in multimedia applications is ever increasing. The image compression plays vital role in multimedia applications. The ultimate aim of image compression is to reduce storage space wit...The amount of image data generated in multimedia applications is ever increasing. The image compression plays vital role in multimedia applications. The ultimate aim of image compression is to reduce storage space without degrading image quality. Compression is required whenever the data handled is huge they may be required to sent or transmitted and also stored. The New Edge Directed Interpolation (NEDI)-based lifting Discrete Wavelet Transfrom (DWT) scheme with modified Set Partitioning In Hierarchical Trees (MSPIHT) algorithm is proposed in this paper. The NEDI algorithm gives good visual quality image particularly at edges. The main objective of this paper is to be preserving the edges while performing image compression which is a challenging task. The NEDI with lifting DWT has achieved 99.18% energy level in the low frequency ranges which has 1.07% higher than 5/3 Wavelet decomposition and 0.94% higher than traditional DWT. To implement this NEDI with Lifting DWT along with MSPIHT algorithm which gives higher Peak Signal to Noise Ratio (PSNR) value and minimum Mean Square Error (MSE) and hence better image quality. The experimental results proved that the proposed method gives better PSNR value (39.40 dB for rate 0.9 bpp without arithmetic coding) and minimum MSE value is 7.4.展开更多
Data compression is one of the core fields of study for applications of image and video processing.The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result,it is desirable to...Data compression is one of the core fields of study for applications of image and video processing.The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result,it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques,the data must be reconstituted very similarly to the initial form.In this paper,a hybrid compression based on Discrete Cosine Transform(DCT),DiscreteWavelet Transform(DWT)is used to enhance the quality of the reconstructed image.These techniques are followed by entropy encoding such as Huffman coding to give additional compression.Huffman coding is optimal prefix code because of its implementation is more simple,faster,and easier than other codes.It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio,Mean Square Error(MSE),and Peak Signal to Noise Ratio(PSNR).We applied a hybrid algorithm on(DWT–DCT 2×2,4×4,8×8,16×16,32×32)blocks.Finally,we show that by using a hybrid(DWT–DCT)compression technique,the PSNR is reconstructed for the image by using the proposed hybrid algorithm(DWT–DCT 8×8 block)is quite high than DCT.展开更多
Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a ch...Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images.Therefore,image compression techniques can be utilized to process remote sensing images.In this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image construction.The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it.With this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing images.The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook.Besides,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is employed.The design of SBO algorithm for remote sensing image compression depicts the novelty of the work.To showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.展开更多
A novel mathematical morphological approach for region of interest(ROI) automatic determination and JPEG2000-based coding of microscopy image compression is presented.The algorithm is very fast and requires lower comp...A novel mathematical morphological approach for region of interest(ROI) automatic determination and JPEG2000-based coding of microscopy image compression is presented.The algorithm is very fast and requires lower computing power,which is particularly suitable for some irregular region-based cell microscopy images with poor qualities.Firstly,an active threshold-based method is discussed to create a rough mask of regions of interest(cells).And then some morphological operations are designed and applied to achieve the segmentation of cells.In addition,an extra morphological operation,dilation,is applied to create the final mask with some redundancies to avoid the"edge effect"after removing false cells.Finally,ROI and region of background(ROB) are obtained and encoded individually in different compression ratio flexibly based on the JPEG2000,which can adjust the quality between ROI and ROB without coding for ROI shape.The experimental results certify the effectiveness of the proposed algorithm,and compared with JPEG2000,the proposed algorithm has better performance in both subjective quality and objective quality at the same compression ratios.展开更多
In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only o...In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.展开更多
Discrete Cosine Transform(DCT)is the most widely used technique in image and video compression.In this paper,the structure of DCT and Inverse DCT(IDCT)algorithm is split in the form of COordinate Rotation DIgital Comp...Discrete Cosine Transform(DCT)is the most widely used technique in image and video compression.In this paper,the structure of DCT and Inverse DCT(IDCT)algorithm is split in the form of COordinate Rotation DIgital Computer(CORDIC)rotation matrix.The two-dimensional(2-D)8×8 DCT/IDCT units based on the improved rotation CORDIC algorithm is proposed.The shift and addition operations of the CORDIC algorithm are used to replace the cosine multiplication operations in the algorithm.The design does not contain any multiplier unit,which reduces the complexity of the hardware unit.The row-column transform unit composed of register arrays connects two 1-D 8-point DCT units to complete the calculation of 2-D 8×8 DCT.The pipeline latency of proposed architecture is 28 clock cycles.The proposed efficient two-dimensional DCT architecture has been synthesized on the Xilinx’s Kintex-7 FPGA.The resource utilization is 17.36%for Slice LUTs,3.49%for Slice Registers,and the maximum operating frequency is 172 MHz.It takes only 0.161μs to complete a process of block of 8×8 samples.A frame of image is processed by the designed DCT unit and then reconstructed by the IDCT unit to verify the function.The Peak Signal to Noise Ratio(PSNR)can reach 51.99 dB.展开更多
Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. ...Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).展开更多
Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is re...Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is restricted or difficult because of cost or technological constraints. In this work, we present a robust imaging method based on compressive imaging that sets two thresholds to select the measurement data for image reconstruction.The experimental and numerical simulation results show that the proposed double-threshold compressive imaging protocol provides better image quality than previous compressive imaging schemes. Faster imaging speeds can be attained using this scheme because it requires less data storage space and computing time. Thus,this denoising method offers a very effective approach to promote the implementation of compressive imaging in real-time practical applications.展开更多
In this paper,a lifted Haar transform(LHT)image compression optical chip has been researched to achieve rapid image compression.The chip comprises 32 same image compression optical circuits,and each circuit contains a...In this paper,a lifted Haar transform(LHT)image compression optical chip has been researched to achieve rapid image compression.The chip comprises 32 same image compression optical circuits,and each circuit contains a 2×2 multimode interference(MMI)coupler and aπ/2 delay line phase shifter as the key components.The chip uses highly borosilicate glass as the substrate,Su8 negative photoresist as the core layer,and air as the cladding layer.Its horizontal and longitudinal dimensions are 8011μm×10000μm.Simulation results present that the designed optical circuit has a coupling ratio(CR)of 0:100 and an insertion loss(IL)of 0.001548 d B.Then the chip is fabricated by femtosecond laser and testing results illustrate that the chip has a CR of 6:94 and an IL of 0.518 d B.So,the prepared chip possesses good image compression performance.展开更多
To achieve high-quality image compression of a floral canopy,a region of interest(ROI)mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the RO...To achieve high-quality image compression of a floral canopy,a region of interest(ROI)mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees(ROI-SPIHT)coding.The embedded zerotree wavelet(EZW)coding was conducted for the background(BG)region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding.Through the weighing factor r of the ROI coding amount,the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded,truncatable bit streams.Despite the location of truncation,the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI.The results indicated that under the same bit rate,the larger the r value is,the larger the peak-signal-to-noise ratio(PSNR)for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image.In the range of 0.07-1.09 bpp,the PSNR of the ROI reconstructed image was 42.65%higher on average than that of the BG reconstructed image,43.95%higher on average than that of the composite image of the ROI and BG(ALL),and 16.84%higher on average than that of the standard SPIHT reconstructed image.Additionally,the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG,ALL,and standard SPIHT reconstructed images.The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image,indicating that the image compression algorithm based on the mask hybrid coding for ROI(ROI-MHC)is capable of improving the reconstruction quality of an ROI image.When the weighing factor r is a fixed value,as the proportion of ROI(a)increases,the quality of ROI image reconstruction gradually decreases.Therefore,upon the application of the ROI-MHC image compression algorithm,high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a.Under the same bit rate,the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities.展开更多
Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-c...Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.展开更多
Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression parameters need to be determined based on the specific quality requirement.In high efficiency video ...Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression parameters need to be determined based on the specific quality requirement.In high efficiency video coding(HEVC),regarded as the state-of-the-art compression tool,the quantization parameter(QP)plays a dominant role in controlling quality.The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results.In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control.Its kernel is a support vector regression(SVR)based learning model that is capable of predicting the optimal QP from both vid-eo-based and structural image features extracted directly from raw data,avoiding time-consuming processes such as pre-encoding and iteration,which are often needed in existing techniques.Experimental results on several datasets verify that our approach outperforms current video-based quality control methods.展开更多
基金supported in part by the National Natural Science Foundation of China (62371414,61901406)the Hebei Natural Science Foundation (F2020203025)+2 种基金the Young Talent Program of Universities and Colleges in Hebei Province (BJ2021044)the Hebei Key Laboratory Project (202250701010046)the Central Government Guides Local Science and Technology Development Fund Projects(216Z1602G)。
文摘Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 71571091,71771112the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds under Grant PAL-N201801the Excellent Talent Training Project of University of Science and Technology Liaoning under Grant 2019RC05.
文摘With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.
文摘In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).
基金This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401)in part by the 2022 Yeungnam University Research Grant.
文摘The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.
基金funded by the University of Jeddah,Saudi Arabia,under Grant No.UJ-20-043-DR。
文摘This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image compression.Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding.It transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a threshold.PSO is applied for selecting a particle assigned to the threshold values for the subbands.Nine positions assigned to particles values are used to represent population.Every particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe value.The experiments are applied on five different medical image types,i.e.,MRI,CT,and X-ray.Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.
基金This work is supported in part by the National Natural Science Foundation of China under contracts 61672242 and 61702199in part by China Spark Program under Grant 2015GA780002+1 种基金in part by The National Key Research and Development Program of China under Grant 2017YFD0701601in part by Natural Science Foundation of Guangdong Province under Grant 2015A030313413.
文摘Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.
文摘Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by
文摘be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.
文摘The amount of image data generated in multimedia applications is ever increasing. The image compression plays vital role in multimedia applications. The ultimate aim of image compression is to reduce storage space without degrading image quality. Compression is required whenever the data handled is huge they may be required to sent or transmitted and also stored. The New Edge Directed Interpolation (NEDI)-based lifting Discrete Wavelet Transfrom (DWT) scheme with modified Set Partitioning In Hierarchical Trees (MSPIHT) algorithm is proposed in this paper. The NEDI algorithm gives good visual quality image particularly at edges. The main objective of this paper is to be preserving the edges while performing image compression which is a challenging task. The NEDI with lifting DWT has achieved 99.18% energy level in the low frequency ranges which has 1.07% higher than 5/3 Wavelet decomposition and 0.94% higher than traditional DWT. To implement this NEDI with Lifting DWT along with MSPIHT algorithm which gives higher Peak Signal to Noise Ratio (PSNR) value and minimum Mean Square Error (MSE) and hence better image quality. The experimental results proved that the proposed method gives better PSNR value (39.40 dB for rate 0.9 bpp without arithmetic coding) and minimum MSE value is 7.4.
文摘Data compression is one of the core fields of study for applications of image and video processing.The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result,it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques,the data must be reconstituted very similarly to the initial form.In this paper,a hybrid compression based on Discrete Cosine Transform(DCT),DiscreteWavelet Transform(DWT)is used to enhance the quality of the reconstructed image.These techniques are followed by entropy encoding such as Huffman coding to give additional compression.Huffman coding is optimal prefix code because of its implementation is more simple,faster,and easier than other codes.It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio,Mean Square Error(MSE),and Peak Signal to Noise Ratio(PSNR).We applied a hybrid algorithm on(DWT–DCT 2×2,4×4,8×8,16×16,32×32)blocks.Finally,we show that by using a hybrid(DWT–DCT)compression technique,the PSNR is reconstructed for the image by using the proposed hybrid algorithm(DWT–DCT 8×8 block)is quite high than DCT.
基金This work was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)National Research Foundation of Korea(NRF)grant funded by the Korea government,Ministry of Science and ICT(MSIT)(2021R1F1A1046339).
文摘Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images.Therefore,image compression techniques can be utilized to process remote sensing images.In this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image construction.The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it.With this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing images.The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook.Besides,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is employed.The design of SBO algorithm for remote sensing image compression depicts the novelty of the work.To showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.
文摘A novel mathematical morphological approach for region of interest(ROI) automatic determination and JPEG2000-based coding of microscopy image compression is presented.The algorithm is very fast and requires lower computing power,which is particularly suitable for some irregular region-based cell microscopy images with poor qualities.Firstly,an active threshold-based method is discussed to create a rough mask of regions of interest(cells).And then some morphological operations are designed and applied to achieve the segmentation of cells.In addition,an extra morphological operation,dilation,is applied to create the final mask with some redundancies to avoid the"edge effect"after removing false cells.Finally,ROI and region of background(ROB) are obtained and encoded individually in different compression ratio flexibly based on the JPEG2000,which can adjust the quality between ROI and ROB without coding for ROI shape.The experimental results certify the effectiveness of the proposed algorithm,and compared with JPEG2000,the proposed algorithm has better performance in both subjective quality and objective quality at the same compression ratios.
文摘In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.
文摘Discrete Cosine Transform(DCT)is the most widely used technique in image and video compression.In this paper,the structure of DCT and Inverse DCT(IDCT)algorithm is split in the form of COordinate Rotation DIgital Computer(CORDIC)rotation matrix.The two-dimensional(2-D)8×8 DCT/IDCT units based on the improved rotation CORDIC algorithm is proposed.The shift and addition operations of the CORDIC algorithm are used to replace the cosine multiplication operations in the algorithm.The design does not contain any multiplier unit,which reduces the complexity of the hardware unit.The row-column transform unit composed of register arrays connects two 1-D 8-point DCT units to complete the calculation of 2-D 8×8 DCT.The pipeline latency of proposed architecture is 28 clock cycles.The proposed efficient two-dimensional DCT architecture has been synthesized on the Xilinx’s Kintex-7 FPGA.The resource utilization is 17.36%for Slice LUTs,3.49%for Slice Registers,and the maximum operating frequency is 172 MHz.It takes only 0.161μs to complete a process of block of 8×8 samples.A frame of image is processed by the designed DCT unit and then reconstructed by the IDCT unit to verify the function.The Peak Signal to Noise Ratio(PSNR)can reach 51.99 dB.
文摘Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).
基金supported by the National Natural Science Foundation of China(Nos.11675014,61605218,61601442,61575207,and 61474123)the National Major Scientific Instruments Development Project of China(No.2013YQ030595)+2 种基金the National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciences,the Science and Technology Innovation Foundation of Chinese Academy of Sciences(No.CXJJ-16S047)the Program of International Science and Technology Cooperation(No.2016YFE0131500)the Advance Research Project(No.30102070101)
文摘Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is restricted or difficult because of cost or technological constraints. In this work, we present a robust imaging method based on compressive imaging that sets two thresholds to select the measurement data for image reconstruction.The experimental and numerical simulation results show that the proposed double-threshold compressive imaging protocol provides better image quality than previous compressive imaging schemes. Faster imaging speeds can be attained using this scheme because it requires less data storage space and computing time. Thus,this denoising method offers a very effective approach to promote the implementation of compressive imaging in real-time practical applications.
基金the Natural Science Foundation of Hubei Province(No.2017CFB685)Hubei University of Technology"Advanced Manufacturing Technology and Equipment"Collaborative Innovation Center Open Research Fund(Nos.038/1201501 and 038/1201803)the College-level Project of Hubei University of Technology(Nos.4201/01758,4201/01802,4201/01889,and 4128/21025)。
文摘In this paper,a lifted Haar transform(LHT)image compression optical chip has been researched to achieve rapid image compression.The chip comprises 32 same image compression optical circuits,and each circuit contains a 2×2 multimode interference(MMI)coupler and aπ/2 delay line phase shifter as the key components.The chip uses highly borosilicate glass as the substrate,Su8 negative photoresist as the core layer,and air as the cladding layer.Its horizontal and longitudinal dimensions are 8011μm×10000μm.Simulation results present that the designed optical circuit has a coupling ratio(CR)of 0:100 and an insertion loss(IL)of 0.001548 d B.Then the chip is fabricated by femtosecond laser and testing results illustrate that the chip has a CR of 6:94 and an IL of 0.518 d B.So,the prepared chip possesses good image compression performance.
基金support provided by the Natural Science Fundation of Jiangsu Province:Youth Fund(Grant No.BK20170727)the Fundamental Research Funds for the Central Universities(Grant No.KYGX201703)the Natural Science Fundation of Jiangsu Province:Youth Fund(Grant No.BK20150686).
文摘To achieve high-quality image compression of a floral canopy,a region of interest(ROI)mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees(ROI-SPIHT)coding.The embedded zerotree wavelet(EZW)coding was conducted for the background(BG)region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding.Through the weighing factor r of the ROI coding amount,the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded,truncatable bit streams.Despite the location of truncation,the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI.The results indicated that under the same bit rate,the larger the r value is,the larger the peak-signal-to-noise ratio(PSNR)for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image.In the range of 0.07-1.09 bpp,the PSNR of the ROI reconstructed image was 42.65%higher on average than that of the BG reconstructed image,43.95%higher on average than that of the composite image of the ROI and BG(ALL),and 16.84%higher on average than that of the standard SPIHT reconstructed image.Additionally,the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG,ALL,and standard SPIHT reconstructed images.The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image,indicating that the image compression algorithm based on the mask hybrid coding for ROI(ROI-MHC)is capable of improving the reconstruction quality of an ROI image.When the weighing factor r is a fixed value,as the proportion of ROI(a)increases,the quality of ROI image reconstruction gradually decreases.Therefore,upon the application of the ROI-MHC image compression algorithm,high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a.Under the same bit rate,the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities.
基金supported by the National Key R&D Program of China(No.:2019YFB1803400)the National Natural Science Foundation of China(Nos.NSFC 61925105,61801260 and U1633121)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.FRF-NP-2003)supported by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.
基金the National Natural Science Foundation of China(No.61890954)。
文摘Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression parameters need to be determined based on the specific quality requirement.In high efficiency video coding(HEVC),regarded as the state-of-the-art compression tool,the quantization parameter(QP)plays a dominant role in controlling quality.The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results.In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control.Its kernel is a support vector regression(SVR)based learning model that is capable of predicting the optimal QP from both vid-eo-based and structural image features extracted directly from raw data,avoiding time-consuming processes such as pre-encoding and iteration,which are often needed in existing techniques.Experimental results on several datasets verify that our approach outperforms current video-based quality control methods.