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image compression scheme based on PCA for wireless multimedia sensor networks

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The Journal of China Universities of Posts and Telecommunications 2016年第1期23卷 22-30页

作者： Zhou Wei Sun Lijuan Guo Jian Liu Linfeng College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003 China

In combination of the characteristic of the network architecture of wireless multimedia sensor networks （WMSNs）, a distributed multi-node cooperative network （DMCN） model is designed by using the concept of in-network processing to improve their energy, memory and computational power. To balance the energy consumption of the network, according to roles division, camera nodes and common nodes are cooperated to accomplish the workload of image acquisition, compression and transmission. Camera nodes gather images and send blocking images to the common nodes in cluster. Common nodes adaptively compress the partitioned images by using a noise-tolerant distributed image compression （NDIC） algorithm based on principal component analysis （PCA） called NDIC-PCA algorithm and send the compressed data to the cluster head node. Then, the cluster head node sends the compressed image data to the station. Simulation results demonstrate that, DCNM can effectively balance the energy consumption of network and largely extend the network lifecycle. In addition, compared with previous algorithms, the proposed NDIC-PCA algorithm achieves higher peak signal to noise ratio without decreasing compression ratio.

关键词： wireless multimedia sensor networks image compression principal component analysis node collaboration

image compression algorithm of floral canopy based on mask hybrid coding for ROI

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 166-176页

作者： Sun Guoxiang Wang Xiaochan Ding Yongqian Li Yuhua Zhang Baohua Li Yongbo Zhang Yu College of Engineering Nanjing Agricultural UniversityNanjing 210095China

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)*** 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 *** 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 *** 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 *** 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 *** 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 ***,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 *** 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 *** the weighing factor r is a fixed value,as the proportion of ROI(a)increases,the quality of ROI image reconstruction gradually ***

关键词： floral canopy ROI mask hybrid coding image compression algorithm wavelet transform

Empirical data decomposition and its applications in image compression

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Journal of Systems Engineering and Electronics 2007年第1期18卷 164-170页

作者： Deng Jiaxian Wu Xiaoqin Information Science and Technology Coll. Hainan Univ. Haikou 570228 E R. China Electric Information Engineering Coll. Beijing Jiaotong Univ. Beijing 100044 P. R. China

A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.

关键词： image processing image compression Empirical data decomposition Non-stationary Nonlinear Data decomposition framework

Fast Adaptive Wavelet for Remote Sensing image compression

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Journal of Computer Science & Technology 2007年第5期22卷 770-778页

作者：李波焦润海李元诚 Digital Media Laboratory School of Computer Science and Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Virtual Reality TechnologiesBeihang UniversityBeijing 100083China Digital Media Laboratory School of Computer Science and Engineering Beihang UniversityBeijing 100083China

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

关键词： wavelet construction remote sensing image image compression energy compaction image classification fast adaptive wavelet selection

An efficient adaptive arithmetic coding image compression technology

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Chinese Physics B 2011年第10期20卷 239-245页

作者：王兴元云娇娇张永雷 Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.

关键词： arithmetic coding adaptive image compression

Collaborative image compression and classification with multi-task learning for visual Internet of Things

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Chinese Journal of Aeronautics 2022年第5期35卷 390-399页

作者： Bing DU Yiping DUAN Hang ZHANG Xiaoming TAO Yue WU Congchong RU School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Computer Science and Technology Xidian UniversityXian 710071China Smart Network Computing Lab Cross-strait Tsinghua Research InstitutionBeijing 100084China

Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and *** onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io ***,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for ***,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 *** particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and *** key to the proposed framework is the quantized latent representation used for compression and *** with perceptual quality can achieve low bitrate compression and reduce the amount of data *** 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 *** extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.

关键词： Deep learning Generative Adversarial Network(GAN) image classification image compression Internet of Things

Adaptive interference hyperspectral image compression with spectrum distortion control

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Chinese Optics Letters 2009年第10期7卷 934-937页

作者：马静李云松吴成柯陈东 State Key Laboratory of Integrated Service Networks Xidian University

As one of the next generation imaging spectrometers, interferential spectrometer has been paid much attention. With traditional spectrum compression methods, the hyperspectral images generated by interferential spectrometer can only be protected with better visual quality in spatial domain, but its optical applications in Fourier domain are often ignored. So the relation between the distortion in Fourier domain and the compression in spatial domain is analyzed in this letter. Based on this analysis, a novel coding scheme is proposed, which can compress data in spatial domain while reducing the distortion in Fourier domain. The bitstream of set partitioning in hierarchical trees （SPIHT） is truncated by adaptively lifting the rate-distortion slopes of zerotrees according to the priorities of optical path difference （OPD） based on rate-distortion optimization theory. Experimental results show that the proposed scheme can achieve better performance in Fourier domain while maintaining the image quality in spatial domain.

关键词： Electric distortion image coding image compression image quality Independent component analysis Signal distortion Site selection Spectrometers Spectrometry

An improved partial SPIHT with classified weighted rate-distortion optimization for interferential multispectral image compression

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Chinese Optics Letters 2008年第5期6卷 331-333页

作者：王柯俨吴成柯孔繁锵张磊 State Key Laboratory of Integrated Service Networks Xidian University

Based on the property analysis of interferential multispectral images, a novel compression algorithm of partial set partitioning in hierarchical trees （SPIHT） with classified weighted rate-distortion optimization is presented. After wavelet decomposition, partial SPIHT is applied to each zero tree independently by adaptively selecting one of three coding modes according to the probability of the significant coefficients in each bitplane. Meanwhile the interferential multispectral image is partitioned into two kinds of regions in terms of luminous intensity, and the rate-distortion slopes of zero trees are then lifted with classified weights according to their distortion contribution to the constructed spectrum. Finally a global rate- distortion optimization truncation is performed. Compared with the conventional methods, the proposed algorithm not only improves the performance in spatial domain but also reduces the distortion in spectral domain.

关键词： Boolean functions Data compression Electric distortion image coding image compression Motion estimation Optimization Programming theory Risk assessment Signal distortion Video signal processing Wavelet decomposition Wavelet transforms

Application of image compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

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Computers, Materials & Continua 2019年第6期59卷 753-775页

作者： Mohammed Omari Souleymane Ouled Jaafri LDDI Laboratory Mathematics and Computer Science DepartmentUniversity of AdrarAdrar 01000Algeria

be stored or transmitted in an efficient *** 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 *** 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 *** 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 *** 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 *** 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.

关键词： image compression simple blurring middle blurring majority blurring similarity classification mean squared error