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Learned Image Compression via Text-Semantic Guidance and Content-Aware Bitrate Control 认领 引用
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作者 Kaisen Li Yunwei Zhang +1 位作者 Guoying Sun Bin Li 《Computers, Materials & Continua》 SCIE EI 2026年第10期1017-1047,共31页
With the development of vision-language pre-trained models,effectively exploiting high-level semantics and precisely controlling bitrate in learned image compression remains a challenging problem.Existing methods main... With the development of vision-language pre-trained models,effectively exploiting high-level semantics and precisely controlling bitrate in learned image compression remains a challenging problem.Existing methods mainly rely on image feature modeling alone,making it difficult to jointly preserve fine-grained details and semantic consistency under a given bitrate budget.To address this issue,this paper proposes a learned image compression framework that integrates text-semantic guidance with content-aware bitrate control.The framework combines Bootstrapping Language-Image Pre-training(BLIP)and Contrastive Language-Image Pre-training(CLIP)to extract image semantic information,and performs conditional modulation on multi-scale visual features through feature-wise linear modulation to construct semantically enhanced latent representations,thereby improving structural preservation and visual reconstruction quality under low-bitrate conditions.Meanwhile,we design a bitrate control module that integrates content awareness with historical feedback.Regional importance is estimated by channel attention,and the quantization strength is adaptively adjusted according to the target and actual bitrates,enabling the model to approach the preset bitrate without manual tuning.Experimental results on different datasets show that,compared with representative learned image compression methods,including Cheng2020,Efficient Learned Image Compression(ELIC),and Frequency-aware Transformer for Learned Image Compression(FTIC),the proposed method achieves a more competitive rate-distortion trade-off.In particular,it consistently exhibits superior rate-distortion(RD)performance,delivers higher objective reconstruction quality at the same bitrate,and maintains favorable subjective perceptual consistency. 展开更多
关键词 Learned image compression feature-wise linear modulation semantic guidance semantically enhanced latent representation content-aware bitrate control
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Learned distributed image compression with decoder side information 认领 引用 被引量：1
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作者 Yankai Yin Zhe Sun +2 位作者 Peiying Ruan Ruidong Li Feng Duan 《Digital Communications and Networks》 SCIE EI CSCD 2025年第2期349-358,共10页
With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communica... With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communication.Multi-view image compression aims to improve compression efficiency by leveraging correlations between images.However,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained devices.In this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding.Our model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image reconstruction.The encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy coding.The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side.Specifically,we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps.Second,we employ a semi-coupled convolution module to extract both shared and specific information in images.Finally,a cross-attention module is employed to fuse mutual information extracted from side information.The effectiveness of our model is validated on various datasets,including KITTI Stereo and Cityscapes.The results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques. 展开更多
关键词 Digital communication Image compression Side information Channel-spatial attention module Cross-attention module
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Exploring High Dimensional Feature Space With Channel-Spatial Nonlinear Transforms for Learned Image Compression 认领 引用 被引量：1
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作者 Wen Tan Fanyang Meng +2 位作者 Chao Li Youneng Bao Yongsheng Liang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第4期1235-1253,共19页
Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by ... Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network widthesolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset. 展开更多
关键词 high dimensional feature space learned image compression nonlinear transform the dimension increase and decrease
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Medical Image Compression Based on Wavelets with Particle Swarm Optimization 认领 引用 被引量：1
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作者 Monagi H.Alkinani E.A.Zanaty Sherif M.Ibrahim 《Computers, Materials & Continua》 SCIE EI 2021年第5期1577-1593,共17页
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
关键词 Image compression wavelets Haar wavelet particle swarm algorithm medical image compression PSNR and CR
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An effective fractal image compression algorithm based on plane fitting 认领 引用 被引量：5
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作者 王兴元 国兴 张丹丹 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第9期149-155,共7页
A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. ... A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio. 展开更多
关键词 fractal image compression plane fitting peak signal to noise ratio gray image
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An improved fast fractal image compression using spatial texture correlation 认领 引用 被引量：4
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作者 王兴元 王远星 云娇娇 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第10期228-238,共11页
This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture f... This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same. 展开更多
关键词 fractal image compression texture features intelligent classification algorithm spatialcorrelation
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A Novel Fractal Wavelet Image Compression Approach 认领 引用 被引量：11
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作者 SONG Chun-lin FENG Rui +1 位作者 LIU Fu-qiang CHEN Xi 《Journal of China University of Mining and Technology》 2007年第1期121-125,共5页
By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the ... By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the different levels of importance accorded the frequency sublevel band wavelet coefficients. Higher frequency sublevel bands would lead to larger initial errors. As a result, the sizes of sublevel blocks and super blocks would be changed according to the initial errors. The matching sizes between sublevel blocks and super blocks would be changed according to the permitted errors and compression rates. Systematic analyses are performed and the experimental results demonstrate that the proposed method provides a satisfactory performance with a clearly increasing rate of compression and speed of encoding without reducing SNR and the quality of decoded images. Simulation results show that our method is superior to the traditional wavelet tree based methods of fractal image compression. 展开更多
关键词 four-fork tree adaptive wavelets fractal image compression
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BFA BASED NEURAL NETWORK FOR IMAGE COMPRESSION 认领 引用 被引量：4
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作者 Chu Ying Mi Hua +2 位作者 Ji Zhen Shao Zibo Q. H. Wu 《Journal of Electronics(China)》 2008年第3期405-408,共4页
A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are... A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the quality of the reconstructed images significantly. 展开更多
关键词 Bacterial Foraging Algorithm （BFA） Artificial Neural Network （ANN） Back Propagation（BP） Image compression
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A hybrid quantum encoding algorithm of vector quantization for image compression 认领 引用 被引量：4
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作者 庞朝阳 周正威 郭光灿 《Chinese Physics B》 CAS 2006年第12期3039-3043,共5页
Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability... Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm. 展开更多
关键词 vector quantization Grover＇s algorithm image compression quantum algorithm
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Nonlinear Adaptive Wavelet Transform for Lossless Image Compression 认领 引用 被引量：2
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作者 ZHANG Dong YANG Yan QIN Qianqing 《Wuhan University Journal of Natural Sciences》 CAS 2007年第2期267-270,共4页
The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear... The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon. 展开更多
关键词 adaptive wavelets lifting scheme nonlinear operator morphological predictor image compression
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Empirical data decomposition and its applications in image compression 认领 引用 被引量：3
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作者 Deng Jiaxian Wu Xiaoqin 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期164-170,共7页
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, i... 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. 展开更多
关键词 Image processing Image compression Empirical data decomposition Non-stationary Nonlinear Data decomposition framework
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An efficient adaptive arithmetic coding image compression technology 认领 引用
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作者 王兴元 云娇娇 张永雷 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第10期239-245,共7页
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 t... 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. 展开更多
关键词 arithmetic coding adaptive image compression
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Multispectral image compression and encryption method based on tensor decomposition in wavelet domain 认领 引用
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作者 XU Dongdong DU Limin DU Yunlong 《High Technology Letters》 EI CAS 2024年第3期244-251,共8页
Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of int... Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of intrinsic image structure.A novel approach is proposed to address these is-sues.Firstly,a chaotic sequence is generated using the Lorenz three-dimensional chaotic mapping to initiate the encryption process,which is XORed with each spectral band of the multispectral image to complete the initial encryption of the image.Then,a two-dimensional lifting 9/7 wavelet transform is applied to the processed image.Next,a key-sensitive Arnold scrambling technique is employed on the resulting low-frequency image.It effectively eliminates spatial redundancy in the multispectral image while enhancing the encryption process.To optimize the compression and encryption processes further,fast Tucker decomposition is applied to the wavelet sub-band tensor.It effectively removes both spectral redundancy and residual spatial redundancy in the multispectral image.Finally,the core tensor and pattern matrix obtained from the decomposition are subjected to entropy encoding,and real-time chaotic encryption is implemented during the encoding process,effectively integrating compression and encryption.The results show that the proposed algorithm is suitable for occasions with high requirements for compression and encryption,and it provides valuable insights for the de-velopment of compression and encryption in multispectral field. 展开更多
关键词 multi-spectral image compression encryption Lorenz three-dimensional chaotic mapping Arnold scrambling transform fast Tucker decomposition
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Application Research of Image Compression Technology in Monitoring System of Rape Crop in Areas of Qinling Mountains 认领 引用
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作者 姚军财 《Agricultural Science & Technology》 CAS 2012年第2期453-456,共4页
[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of gua... [Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of guaranteeing image quality in the rape crop monitoring system in Qinling Mountains. [Method] In the proposal, the color image was divided into brightness images with three fundamental colors, followed by sub-image division and DCT treatment. Then, coefficients of transform domain were quantized, and encoded and compressed as per Huffman coding. Finally, decompression was conducted through inverse process and decompressed images were matched. [Result] The simulation results show that when compression ratio of the color image of rape crops was 11.972 3∶1, human can not distinguish the differences between the decompressed images and the source images with naked eyes; when ratio was as high as 53.565 6∶1, PSNR was still above 30 dD,encoding efficiency achieved over 0.78 and redundancy was less than 0.22. [Conclusion] The results indicate that the proposed color image compression technology can achieve higher compression ratio on the premise of good image quality. In addition, image encoding quality and decompressed images achieved better results, which fully met requirement of image storage and transmission in monitoring system of rape crop in the Qinling Mountains. 展开更多
关键词 Image compression Rape crop Discrete Cosine Transform Peak Signal Noise Ratio Compression ratio
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Collaborative image compression and classification with multi-task learning for visual Internet of Things 认领 引用 被引量：2
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作者 Bing DU Yiping DUAN +3 位作者 Hang ZHANG Xiaoming TAO Yue WU Congchong RU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第5期390-399,共10页
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
关键词 Deep learning Generative Adversarial Network(GAN) Image classification Image compression Internet of Things
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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System 认领 引用 被引量：3
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作者 Zhiqing Dong Zhao Zhang +1 位作者 Hongyan Zhou Xuebo Chen 《Computers, Materials & Continua》 SCIE EI 2024年第2期1977-1993,共17页
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
关键词 Image encryption image compression hyperchaotic system compressed sensing
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Satellite Data Reduction Using Entropy-preserved Image Compression Technique 认领 引用 被引量：1
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作者 李俊 周凤仙 高清怀 《Advances in Atmospheric Sciences》 SCIE CAS 1991年第2期237-242,共6页
In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the f... In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising. 展开更多
关键词 Jun Li Satellite Data Reduction Using Entropy-preserved Image Compression Technique line node than
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A mathematical morphological approach for region of interest coding of microscopy image compression 认领 引用
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作者 夏伟强 樊尚春 +3 位作者 邢维巍 刘长庭 李天志 王俊峰 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2012年第3期115-121,共7页
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
关键词 mathematical morphology region of interest(ROI) automatic segmentation microscopy image compression JPEG2000
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Fabricating lifted Haar transform image compression optical chip based on femtosecond laser 认领 引用
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作者 TAO Qing WEI Liangpeng +3 位作者 KUANG Wenxiang YIN Yegang CHENG Jian LIU Dun 《Optoelectronics Letters》 EI 2023年第9期519-525,共7页
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
关键词 Fabricating lifted Haar transform image compression optical chip based on femtosecond laser image
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Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring 认领 引用
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作者 Mohammed Omari Souleymane Ouled Jaafri 《Computers, Materials & Continua》 SCIE EI 2019年第6期753-775,共23页
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
关键词 Image compression simple blurring middle blurring majority blurring similarity classification mean squared error
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