Although several well known compression techniques exist , many of them require computationally intensive algorithms . In addition , many image compression techniques introduce unwanted attributes such as the blocking effect or noise . This is a major problem in medical imaging where image degradation may be critical . This paper presents wavelet based medical image coding which is adopted from JPEG2000 Part 1 image coding standard to provide lossless image compression . The image compression scheme is implemented in a grid computing system to aim a powerful computing capability and to cut down computing time . Wavelet-based image processing naturally fit with grid computing because the original image can be split into smaller blocks , which later can be processed independently . Character extraction is a very important task in document image process and recognition . It 's also a main step in our image compression scheme for flight coupon images storage . In this paper we improve a segmentation method based on hierarchical layout analysis to extract variable printed characters . An overview of ongoing research related to the development of an image data compression algorithm using artificial neural networks ( ANNs ) is presented . The data compression technique under study uses an ANN to perform vector quantization ( VQ ) . A highly performing image data compression technique is currently being developed for space science applications under the requirement of high-speed and pushbroom scanning . The technique is also applicable to frame based imaging data . In this paper we source encode the truncated block used in block truncation coding . It is shown that the truncated block is well approximated by wide-sense Markoff statistics ; a signal having these characteristics has a high probability of belonging to the root signal set of median filters . ABSTRACT Block Truncation Coding is one of the easy and efficient techniques for lossy image compression . In this paper , we have proposed few methods to enhance the existing Interpolative and Lookup procedures to reduce the bitrate and to improve the PSNR obtained with the Block Truncation Coding ( BTC ) based image compression methods . ABSTRACT Block Truncation Coding ( BTC ) is a fast moment preserving lossy image compression technique . This paper proposes a original image . Content based image classification is a vital component of machine learning and is attaining increasing importance in the field of image processing . This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification . A new block truncation coding ( BTC ) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification . In the first stage of this scheme , each three-dimensional color image is reduced to a one-dimensional gray-level image using the moment-preserving thresholding technique . Then , a new edge detection technique based on the block truncation coding scheme is introduced to detect the edge boundary in the second stage . The edge detection process makes use of the bit plane information of each BTC-encoded block to detect the edge boundary . Abstract This paper presents a modified block truncation coding scheme for the compression of images . We first design a set of binary edge patterns , which are visually significant , to approximate the bit plane of an image block . The average bit rate is reduced by making use of the special constraint among the pixels in an oriented model , where the intensity variation is along only one direction . Judging from the image quality around edges , the reconstructed images with a bit rate less than 0.68 bit/pixel are perceptually superior to those obtained by the block and truncation coding at a bit rate of 1.64 bits/pixel . The complexity of this algorithm increases linearly with the size of the block and the number of orientations considered , which is much lower than that of the standard vector quantization techniques.<<ETX > > A new method is introduced by which a block truncation coder ( BTC ) is cascaded with a neural network-based vector quantizer ( VQ ) . The proposed coder is very attractive for real time image transmission due to its simplicity and performance . Most normally used features to signify images are Color , texture and shape . Here the Block truncation coding feature known as ( BTCF ) is used so as to compress the image . Further it give thought of Support Vector Machine also known ( SVM ) classifier . In this paper , we have proposed an extended version of Absolute Moment Block Truncation Coding ( AMBTC ) to compress images . Generally the elements of a bitplane used in the variants of Block Truncation Coding ( BTC ) are of size 1 bit . But it has been extended to two bits in the proposed method . Quantization and PCA operation are used to generate the final compact hash securely . In this paper , we propose a novel perceptual image hashing scheme based on block truncation coding ( BTC ) . In the proposed scheme , the pre-processing is first applied on original image through bilinear interpolation , Gaussian low-pass filtering , and singular value decomposition ( SVD ) to construct a secondary image for regularization . An image retrieval based on texture primitive is proposed in this paper . The texture primitive is firstly constructed according to the principle of the block truncation code ( BTC ) . Meantime , based on the analysis of the statistical distribution of the texture primitive , the spatial distribution map of each feature is presented to describe the image texture information . This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification . A new block truncation coding ( BTC ) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification . The performances of classifier algorithms are compared in Receiver Operating Characteristic ( ROC ) Space . In this approach , the composite picture is divided into 16 * 16 pixel blocks , which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 * 16 neural net . For compression purposes , each image block is further divided into 4 * 4 subblocks and , similar to the classical block truncation coding ( BTC ) scheme , a one-bit nonparametric quantizer is used to encode 16 * 16 character and 4 * 4 image blocks . In this case , however , the binary map and quantizer levels are obtained through a neural net segmentor over each block . ABSTRACT Block Truncation Coding ( BTC ) is a fast moment preserving lossy image compression technique . This paper proposes a original image . However , to claim integrity guarantee of image authentication techniques is meaningful only when the false negative probability is taken into consideration . In this paper , the security of an image authentication scheme based on Block-Truncation-Coding ( BTC ) compression proposed by Li et al. is analyzed . The false alarm of their scheme is shown unneglectable in this paper . Abstract This paper describes a new single bit-map block truncation coding ( SBBTC ) scheme that works with a Hopfield neural network ( HNN ) for the coding of color images . An incoming color block is encoded using three block-truncation coding ( BTC ) encoders with a common bit map for each of the three primary color planes . An HNN is used to generate a good single bit map in the SBBTC by clustering the color vectors in the block into two classes . The wavelet transform module performs multi-level wavelet transform on an original image ; the address mapping module maps a transform coefficient corresponding to the address of a wavelet decomposed image to an address stored in a specific wavelet coefficient storing region ; the wavelet coefficient storing regions store wavelet coefficients calculated by the wavelet transform module ; arbitration module for a code-block to be coded temporarily stores the code-block information not processed subsequently , and prepares the wavelet coefficients in a completely stored code-block for bit planar coding ; the bit planar coding module codes and updates the coding state variables . The invention has the advantages of good image compression quality and small system occupancy storage capacity , and can be used for image compression coding for various digital equipments , especially for compression systems of high resolution images . In deep communications systems bandwidth availability , storage and computational capacity play a crucial role and represent precious , as well as limited , communications resources . Starting from this consideration , high efficient image compression coding algorithms may represent a key solution to optimize the resources employment . In this paper two possible approaches have been considered : JPEG2000 and CCSDS Image Compression , which is specifically designed for satellite and deep space communications . This paper is mainly about the concept , principle and classification of image compression coding , introduces the concepts and characteristic of three kinds of image compression coding methods that are common used , and discusses how to using matlab to accomplish the two common methods which mentions in the front . That yields minimal compression for small images and creates great sensitivity to transmission errors . The new image compression format , in contrast , uses a fixed pre-grown hyperspace pattern library . That provides much higher compression ratios , increased error resistance , and even optional encryption . We analyze the influence of the number of filter parameters and use non-stationary multi-resolution decomposition where different wavelet filters are used at different levels of the decomposition . Using JPEG and JPEG2000 compression we assess the normalized correlation and Peak Signal to Noise Ratio ( PSNR ) behavior of the watermarks . The security against unauthorized detection is also investigated . In ROI mode , the input image is represented by the region of interest window . The high quality text compression function with a high compression ratio shows that the proposed system can be comparable with the JPEG2000 products . This system also uses gray coding to improve the compression ratio . According to JPEG2000 image encryption algorithm based on confusion of the wavelet coefficients can not support region-of-interest(ROI ) image coding , an encryption method for ROI images is proposed . The proposed method Generates the confusion table using Chaotic sequences , confuses the Wavelet coefficients inside each code blocks , and encrypts the sign bits using Chaotic sequences . Quality of reconstructed images depends on the compression ratio of the residual image . The obtained results are encouraging and the choice between JPEG2000 metadata embedding and MXF metadata still to be done . A framework for evaluating watermarking robustness to JPEG2000 based content adaptation attacks is presented . The proposed framework represents commonly used wavelet based watermarking algorithms as a subset of a general watermarking framework and simulates the content adaptation modes based on JPEG2000 transcoding to provide a general framework for evaluating watermark robustness in such adaptations and the influence of different embedding modes . In Motion JPEG2000 , video data are compressed in frames according to JPEG2000 part1 . In JPEG2000 part1 encoding , an input image is coded in the order of significant sub-bands , which are low-frequency bands . The proposed transmission system therefore assigns each sub-band data suitable weights according to their significance by using orthogonal multi-code CDMA so that bit errors are concentrated in the high-frequency sub-bands which are of little significance on image reconstruction . The contribution of this paper is the development of a fast , subband-based JPEG2000 image indexing system in the compressed domain which achieves high memory efficiency . This is the extended work on a previously block-based indexing system . A set of metrics , customized for image processing , is proposed to quantitatively evaluate algorithm characteristics . In addition , we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching , linear feature extraction , JPEG2000 image encoding , and nonphotorealistic rendering ( NPR ) as our example applications . The algorithms are carefully selected from major domains of image processing , so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU . The system schemes of the multilevel block-based wavelet transform and the multilevel stripe-based wavelet transform for JPEG2000 are presented . The proposed schemes can effectively control the wavelet coefficient output pattern , and thus reduce the memory usage of JPEG2000 coding buffer . Compared with the line-based wavelet transform , the proposed schemes reduce more than 50 % memory usage of the JPEG2000 system and slightly decrease the memory bandwidth . An embedded RISC processor , a dedicated wavelet transform engine , three entropy codec and on-chip memory system are integrated in ADV212 . It provides a complete JPEG2000 compression and decompression solution for real-time application of standard or high resolution video or still image . The ADV212 implements the function described in the JPEG2000 Standard Part I except the ROI . In the transmission of still images , aimed at the problem of impairing dependency of noised digital image , the paper gives an improvement of JPEG encoding compression that code the image with JPEG basic system after weakening noise process of image with noise model of digital image and implement it using VC++6.0.The results of experiment show that this compression concept provides better impressive performance : high signal-to-noise-ratio and compression-ratio , and also has the value of a wide range of applications . There must be a comprehension for the storage of orbital images , not only because of its size , but also because of the amount of images . This work evaluates the use of JPEG pattern which is very robust and that gives a high rate of compression . For this evaluation , an implementation of the JPEG pattern has been developed which uses three space of colors : RGB , HIS and YCBCR and compares four different algorithms for the transformed of the cosine : using the expression of the transformed ( S ) , using the product of matrix ( PM ) , using factorization of the matrix of cosine which we call optimized ( O ) and also the progressive codification ( P ) of the transformed image . This paper presents an architecture of high-performance JPEG CODEC circuit that can process one pixel per cycle . We reduced the circuit size by using a single transpose buffer and two-stage pipeline for DCT . The system can transmit extra high quality digital full color movies of 2048 by 2048 pixel resolution using 622 Mbps ATM transmission systems , and display them at the frame rate of 60 frames per second . The system consists of an image data server , ATM transport interfaces , a real-time JPEG decoder and an SHD image display frame buffer . The motion SHD images have such high quality that the system has been designed for professional movie applications such as tele- medicine , education and commercial movie theaters . In this paper , we present a new joint watermarking-compression scheme the originality of which stands in the combination of the lossless compression standard JPEG-LS with the bit substitution watermarking modulation . This scheme allows the access to watermarking-based security services without decompressing the image . However , this approach can be inefficient , as reading full-size source images for resizing uses more bandwidth than reading pre-resized images . We propose repurposing the progressive JPEG standard and customizing the organization of image data to reduce the bandwidth overheads of dynamic resizing . We show that at a PSNR of 32 dB , dynamic resizing with progressive JPEG provides 2.5 × read data savings over baseline JPEG , and that progressive JPEG with customized encode parameters can further improve these savings ( up to 5.8 × over the baseline ) . The compression scheme is based on a block adaptive technique for decorrelating the color planes followed by a spatial lossy compression of the decorrelated data . A preferred method of lossy compression is the DCT-based JPEG compression standard , as it is shown that the block adaptive decorrelation operations can be efficiently performed in the DCT domain . The result of the compression technique are compared to that of using JPEG on RGB data without any decorrelating transform . In our proposed method , a statistical model of the DC coefficients from singly compressed JPEG image is used to generate an estimated image which is free from quantization artifacts that are present in the IPM . The estimated image is subjected to NAD-JPEG compression . It was found that the DC values of NAD-JPEG image are no longer be the multiples of the corresponding primary quantization step size \(q_1\ ) . However , with the existing JPEG steganography , the minor changes due to the hidden secret data are not easy to be explored directly from the quantized block DCT ( BDCT ) coefficients in that the energy of the carrier image is much larger than that of the hidden signal . In this paper , we present an improved calibration-based universal JPEG steganalysis , where the microscopic and macroscopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image . All features in our method are generated from the difference signal between the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image , or calculated as the difference between the signal extracted from test image and its corresponding macroscopic calibrated image . Two novel scalable algorithms to construct union-free hypergraphs are proposed and described . Evaluation on a Motion-JPEG image compression core using a Xilinx 7-series-based FPGA hardware platform demonstrates a statistically significant increase in fault tolerance and area efficiency when using proposed work compared to commonly-used modular redundancy approaches .
Although several well known compression techniques exist , many of them require computationally intensive algorithms . In addition , many image compression techniques introduce unwanted attributes such as the blocking effect or noise . This is a major problem in medical imaging where image degradation may be critical .
This paper presents wavelet based medical image coding which is adopted from JPEG2000 Part 1 image coding standard to provide lossless image compression . The image compression scheme is implemented in a grid computing system to aim a powerful computing capability and to cut down computing time . Wavelet-based image processing naturally fit with grid computing because the original image can be split into smaller blocks , which later can be processed independently .
Character extraction is a very important task in document image process and recognition . It 's also a main step in our image compression scheme for flight coupon images storage . In this paper we improve a segmentation method based on hierarchical layout analysis to extract variable printed characters .
An overview of ongoing research related to the development of an image data compression algorithm using artificial neural networks ( ANNs ) is presented . The data compression technique under study uses an ANN to perform vector quantization ( VQ ) .
A highly performing image data compression technique is currently being developed for space science applications under the requirement of high-speed and pushbroom scanning . The technique is also applicable to frame based imaging data .
In this paper we source encode the truncated block used in block truncation coding . It is shown that the truncated block is well approximated by wide-sense Markoff statistics ; a signal having these characteristics has a high probability of belonging to the root signal set of median filters .
ABSTRACT Block Truncation Coding is one of the easy and efficient techniques for lossy image compression . In this paper , we have proposed few methods to enhance the existing Interpolative and Lookup procedures to reduce the bitrate and to improve the PSNR obtained with the Block Truncation Coding ( BTC ) based image compression methods .
ABSTRACT Block Truncation Coding ( BTC ) is a fast moment preserving lossy image compression technique . This paper proposes a original image .
Content based image classification is a vital component of machine learning and is attaining increasing importance in the field of image processing . This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification . A new block truncation coding ( BTC ) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification .
In the first stage of this scheme , each three-dimensional color image is reduced to a one-dimensional gray-level image using the moment-preserving thresholding technique . Then , a new edge detection technique based on the block truncation coding scheme is introduced to detect the edge boundary in the second stage . The edge detection process makes use of the bit plane information of each BTC-encoded block to detect the edge boundary .
Abstract This paper presents a modified block truncation coding scheme for the compression of images . We first design a set of binary edge patterns , which are visually significant , to approximate the bit plane of an image block .
The average bit rate is reduced by making use of the special constraint among the pixels in an oriented model , where the intensity variation is along only one direction . Judging from the image quality around edges , the reconstructed images with a bit rate less than 0.68 bit/pixel are perceptually superior to those obtained by the block and truncation coding at a bit rate of 1.64 bits/pixel . The complexity of this algorithm increases linearly with the size of the block and the number of orientations considered , which is much lower than that of the standard vector quantization techniques.<<ETX > >
A new method is introduced by which a block truncation coder ( BTC ) is cascaded with a neural network-based vector quantizer ( VQ ) . The proposed coder is very attractive for real time image transmission due to its simplicity and performance .
Most normally used features to signify images are Color , texture and shape . Here the Block truncation coding feature known as ( BTCF ) is used so as to compress the image . Further it give thought of Support Vector Machine also known ( SVM ) classifier .
In this paper , we have proposed an extended version of Absolute Moment Block Truncation Coding ( AMBTC ) to compress images . Generally the elements of a bitplane used in the variants of Block Truncation Coding ( BTC ) are of size 1 bit . But it has been extended to two bits in the proposed method .
Quantization and PCA operation are used to generate the final compact hash securely . In this paper , we propose a novel perceptual image hashing scheme based on block truncation coding ( BTC ) . In the proposed scheme , the pre-processing is first applied on original image through bilinear interpolation , Gaussian low-pass filtering , and singular value decomposition ( SVD ) to construct a secondary image for regularization .
An image retrieval based on texture primitive is proposed in this paper . The texture primitive is firstly constructed according to the principle of the block truncation code ( BTC ) . Meantime , based on the analysis of the statistical distribution of the texture primitive , the spatial distribution map of each feature is presented to describe the image texture information .
This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification . A new block truncation coding ( BTC ) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification . The performances of classifier algorithms are compared in Receiver Operating Characteristic ( ROC ) Space .
In this approach , the composite picture is divided into 16 * 16 pixel blocks , which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 * 16 neural net . For compression purposes , each image block is further divided into 4 * 4 subblocks and , similar to the classical block truncation coding ( BTC ) scheme , a one-bit nonparametric quantizer is used to encode 16 * 16 character and 4 * 4 image blocks . In this case , however , the binary map and quantizer levels are obtained through a neural net segmentor over each block .
ABSTRACT Block Truncation Coding ( BTC ) is a fast moment preserving lossy image compression technique . This paper proposes a original image .
However , to claim integrity guarantee of image authentication techniques is meaningful only when the false negative probability is taken into consideration . In this paper , the security of an image authentication scheme based on Block-Truncation-Coding ( BTC ) compression proposed by Li et al. is analyzed . The false alarm of their scheme is shown unneglectable in this paper .
Abstract This paper describes a new single bit-map block truncation coding ( SBBTC ) scheme that works with a Hopfield neural network ( HNN ) for the coding of color images . An incoming color block is encoded using three block-truncation coding ( BTC ) encoders with a common bit map for each of the three primary color planes . An HNN is used to generate a good single bit map in the SBBTC by clustering the color vectors in the block into two classes .
The wavelet transform module performs multi-level wavelet transform on an original image ; the address mapping module maps a transform coefficient corresponding to the address of a wavelet decomposed image to an address stored in a specific wavelet coefficient storing region ; the wavelet coefficient storing regions store wavelet coefficients calculated by the wavelet transform module ; arbitration module for a code-block to be coded temporarily stores the code-block information not processed subsequently , and prepares the wavelet coefficients in a completely stored code-block for bit planar coding ; the bit planar coding module codes and updates the coding state variables . The invention has the advantages of good image compression quality and small system occupancy storage capacity , and can be used for image compression coding for various digital equipments , especially for compression systems of high resolution images .
In deep communications systems bandwidth availability , storage and computational capacity play a crucial role and represent precious , as well as limited , communications resources . Starting from this consideration , high efficient image compression coding algorithms may represent a key solution to optimize the resources employment . In this paper two possible approaches have been considered : JPEG2000 and CCSDS Image Compression , which is specifically designed for satellite and deep space communications .
This paper is mainly about the concept , principle and classification of image compression coding , introduces the concepts and characteristic of three kinds of image compression coding methods that are common used , and discusses how to using matlab to accomplish the two common methods which mentions in the front .
That yields minimal compression for small images and creates great sensitivity to transmission errors . The new image compression format , in contrast , uses a fixed pre-grown hyperspace pattern library . That provides much higher compression ratios , increased error resistance , and even optional encryption .
We analyze the influence of the number of filter parameters and use non-stationary multi-resolution decomposition where different wavelet filters are used at different levels of the decomposition . Using JPEG and JPEG2000 compression we assess the normalized correlation and Peak Signal to Noise Ratio ( PSNR ) behavior of the watermarks . The security against unauthorized detection is also investigated .
In ROI mode , the input image is represented by the region of interest window . The high quality text compression function with a high compression ratio shows that the proposed system can be comparable with the JPEG2000 products . This system also uses gray coding to improve the compression ratio .
According to JPEG2000 image encryption algorithm based on confusion of the wavelet coefficients can not support region-of-interest(ROI ) image coding , an encryption method for ROI images is proposed . The proposed method Generates the confusion table using Chaotic sequences , confuses the Wavelet coefficients inside each code blocks , and encrypts the sign bits using Chaotic sequences .
Quality of reconstructed images depends on the compression ratio of the residual image . The obtained results are encouraging and the choice between JPEG2000 metadata embedding and MXF metadata still to be done .
A framework for evaluating watermarking robustness to JPEG2000 based content adaptation attacks is presented . The proposed framework represents commonly used wavelet based watermarking algorithms as a subset of a general watermarking framework and simulates the content adaptation modes based on JPEG2000 transcoding to provide a general framework for evaluating watermark robustness in such adaptations and the influence of different embedding modes .
In Motion JPEG2000 , video data are compressed in frames according to JPEG2000 part1 . In JPEG2000 part1 encoding , an input image is coded in the order of significant sub-bands , which are low-frequency bands . The proposed transmission system therefore assigns each sub-band data suitable weights according to their significance by using orthogonal multi-code CDMA so that bit errors are concentrated in the high-frequency sub-bands which are of little significance on image reconstruction .
The contribution of this paper is the development of a fast , subband-based JPEG2000 image indexing system in the compressed domain which achieves high memory efficiency . This is the extended work on a previously block-based indexing system .
A set of metrics , customized for image processing , is proposed to quantitatively evaluate algorithm characteristics . In addition , we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching , linear feature extraction , JPEG2000 image encoding , and nonphotorealistic rendering ( NPR ) as our example applications . The algorithms are carefully selected from major domains of image processing , so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU .
The system schemes of the multilevel block-based wavelet transform and the multilevel stripe-based wavelet transform for JPEG2000 are presented . The proposed schemes can effectively control the wavelet coefficient output pattern , and thus reduce the memory usage of JPEG2000 coding buffer . Compared with the line-based wavelet transform , the proposed schemes reduce more than 50 % memory usage of the JPEG2000 system and slightly decrease the memory bandwidth .
An embedded RISC processor , a dedicated wavelet transform engine , three entropy codec and on-chip memory system are integrated in ADV212 . It provides a complete JPEG2000 compression and decompression solution for real-time application of standard or high resolution video or still image . The ADV212 implements the function described in the JPEG2000 Standard Part I except the ROI .
In the transmission of still images , aimed at the problem of impairing dependency of noised digital image , the paper gives an improvement of JPEG encoding compression that code the image with JPEG basic system after weakening noise process of image with noise model of digital image and implement it using VC++6.0.The results of experiment show that this compression concept provides better impressive performance : high signal-to-noise-ratio and compression-ratio , and also has the value of a wide range of applications .
There must be a comprehension for the storage of orbital images , not only because of its size , but also because of the amount of images . This work evaluates the use of JPEG pattern which is very robust and that gives a high rate of compression . For this evaluation , an implementation of the JPEG pattern has been developed which uses three space of colors : RGB , HIS and YCBCR and compares four different algorithms for the transformed of the cosine : using the expression of the transformed ( S ) , using the product of matrix ( PM ) , using factorization of the matrix of cosine which we call optimized ( O ) and also the progressive codification ( P ) of the transformed image .
This paper presents an architecture of high-performance JPEG CODEC circuit that can process one pixel per cycle . We reduced the circuit size by using a single transpose buffer and two-stage pipeline for DCT .
The system can transmit extra high quality digital full color movies of 2048 by 2048 pixel resolution using 622 Mbps ATM transmission systems , and display them at the frame rate of 60 frames per second . The system consists of an image data server , ATM transport interfaces , a real-time JPEG decoder and an SHD image display frame buffer . The motion SHD images have such high quality that the system has been designed for professional movie applications such as tele- medicine , education and commercial movie theaters .
In this paper , we present a new joint watermarking-compression scheme the originality of which stands in the combination of the lossless compression standard JPEG-LS with the bit substitution watermarking modulation . This scheme allows the access to watermarking-based security services without decompressing the image .
However , this approach can be inefficient , as reading full-size source images for resizing uses more bandwidth than reading pre-resized images . We propose repurposing the progressive JPEG standard and customizing the organization of image data to reduce the bandwidth overheads of dynamic resizing . We show that at a PSNR of 32 dB , dynamic resizing with progressive JPEG provides 2.5 × read data savings over baseline JPEG , and that progressive JPEG with customized encode parameters can further improve these savings ( up to 5.8 × over the baseline ) .
The compression scheme is based on a block adaptive technique for decorrelating the color planes followed by a spatial lossy compression of the decorrelated data . A preferred method of lossy compression is the DCT-based JPEG compression standard , as it is shown that the block adaptive decorrelation operations can be efficiently performed in the DCT domain . The result of the compression technique are compared to that of using JPEG on RGB data without any decorrelating transform .
In our proposed method , a statistical model of the DC coefficients from singly compressed JPEG image is used to generate an estimated image which is free from quantization artifacts that are present in the IPM . The estimated image is subjected to NAD-JPEG compression . It was found that the DC values of NAD-JPEG image are no longer be the multiples of the corresponding primary quantization step size \(q_1\ ) .
However , with the existing JPEG steganography , the minor changes due to the hidden secret data are not easy to be explored directly from the quantized block DCT ( BDCT ) coefficients in that the energy of the carrier image is much larger than that of the hidden signal . In this paper , we present an improved calibration-based universal JPEG steganalysis , where the microscopic and macroscopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image . All features in our method are generated from the difference signal between the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image , or calculated as the difference between the signal extracted from test image and its corresponding macroscopic calibrated image .
Two novel scalable algorithms to construct union-free hypergraphs are proposed and described . Evaluation on a Motion-JPEG image compression core using a Xilinx 7-series-based FPGA hardware platform demonstrates a statistically significant increase in fault tolerance and area efficiency when using proposed work compared to commonly-used modular redundancy approaches .
Medical image compression using wavelet transforms and variable block size coding The Implementation of Wavelet-based Medical Image Compression Using JPEG2000 Image Coding Standard on a Grid Computing Scheme Utilizing Condor Distributed Batch System Flight Coupon Characters Extraction Based on Hierarchical Layout Analysis Comparison of prediction methods for differential image processing applications A High Performance Image Data Compression Technique for Space Applications BTC Image Coding Using Median Filter Roots Block Truncation Coding using Enhanced Interpolations and Lookup Procedures for Image Compression Enhanced AMBTC for Image Compression using Block Classification and Interpolation Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers An Adaptive Two-Stage Edge Detection Scheme for Digital Color Images Modified block truncation coding for image compression Edge-preserving image coding based on local modeling of images Block truncation coding using neural network-based vector quantization for image compression Image Retrieval by using histogram equalization and DBTCF using SVZ Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC A novel image hashing scheme with perceptual robustness using block truncation coding Image Retrieval Based on Spatial Feature of Texture Primitive Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers A neural net based architecture for the segmentation of mixed gray-level and binary pictures Enhanced AMBTC for Image Compression using Block Classification and Interpolation On the security of a BTC-based-compression image authentication scheme Single Bit-Map Block Truncation Coding of Color Images Using a Hopfield Neural Network Low storage coder based on code block in JPEG2000 standard and its coding method Comparative Analysis of Image Compression Algorithms for Deep Space Communications Systems Analysis of the Image Compression Coding Method Autosophy still image compression Key-dependent pyramidal wavelet domains for secure watermark embedding A Still Image Compression System with a High Quality Text Compression Capability An Encryption Algorithm for ROI Images Toward a stereoscopic encoder/decoder for digital cinema Evaluation of watermark robustness to JPEG2000 based content adaptation attacks Novel Motion-JPEG2000 video transmission system over CDMA environment Wavelet-based texture features can be extracted efficiently from compressed-domain for JPEG2000 coded images Design and Performance Evaluation of Image Processing Algorithms on GPUs Memory Efficient Multilevel Discrete Wavelet Transform Schemes for JPEG2000 ADV212-based High Resolution Still Image Compression System Design A New Research of Still Image Encoding Compression Algorithm A INFLUÊNCIA DO ESPAÇO DE CORES NA COMPRESSÃO JPEG DE IMAGENS ORBITAIS SoC Platform을 이용한 고성능 JPEG IP 구현 및 검증 Super-high-definition digital movie system Joint watermarking and lossless JPEG-LS compression for medical image security Customizing Progressive JPEG for Efficient Image Storage Improved compression technique for multipass color printers Anti-forensics of a NAD-JPEG Detection Scheme Using Estimation of DC Coefficients Calibration based universal JPEG steganalysis Hypergraph-Cover Diversity for Maximally-Resilient Reconfigurable Systems
5689974d3b15e281eb40f61b1400e23a6e624255 9b9ef204c0bed114dbadf34f81619b7ca1234889 927d337d4c4d32cff0aec57dce2259d3fa6c00b1 c919a0ae9ca5ff3d48cdd9ff8801a1969cf047aa f9fa8e5800a43909c0cb835107b257f2960abefe ae0f6c707b930e5ad40582a7f1b6b40925eab0f7 710ca0800385389754567229058928fafff028e4 d6764782a06549bc824365b5c025909b04d14204 5c5e54f4aee9e51fde0c3cdbc4de3c36117e8a6e 8a6295b3a995f98ce2d6c0610b7faf8a17a51889 47be952f61481b3a7867faee7756af9c61e4cb9b 6b954d242e8206d2d7f462f928cb587a044448f7 31c34aa1978f4dbc46af0bae0054f11f3260876f 4d78b527e5e528ec483945882d9534c57b4f5d1b e4c36ca6048f09bd18fc9f4519cbf445e64861ec 1f82167e33613159e7fbfe436abe437e603dcfaa 90cec780a7edfd3f7d2c2607a09e3c73d38bd0fb 5c5e54f4aee9e51fde0c3cdbc4de3c36117e8a6e ed683a992b47af7e262ad495e29baaa2b2b0cc9d d6764782a06549bc824365b5c025909b04d14204 794a7c05cf760dc33c6627aa7c0da6b7108af72b 5ac47c33c41e0e5f3a97356c5451d42c63921619 bc8a241911e074205da7e385e47d052770f2ac45 60df24f79097701e34f5af83e9ea7f293a2b3c83 aa772c4fc907a9493b2c3b2556ef102d7846c5e6 8b6c431937083c19bf2e1d4c08d52ea4bd14b9b8 aa848e3281a910e92d2d3ef4a32d50a92605fbfc d33757bea43a6f55d03fdd4202d5c8dd237d09c2 da532212758e4d0ceb73b26453f1c398391928bd c92d74da9bbbe1e98ad387228205dcf7a3b67998 7937c7ec81e34dcc2ebde26aacbfd78df4e898ae 1b63a78e28f5b53007bfc7a87bda158ce48de043 998876d1b62eb51c874e58beb25637e5a45d1a2a e92a8b56620aa1fe7df55b3a113696afd3e616a0 749f9d76a6d4890bdb44a12574c72901b94340a4 2ac284a213e55121fffc1d04499aa53ad0792c5c 7cf99f6785b13931e233c692cb2ab87a59d97a9c 053be0bf2c952466db7f8158ee9232c2ce4e5d35 01c1e773fff41dd2d24dddedab3f3608f7df82d6 c1fb0de8b3264799df6c8b9e07703e414e5fe3eb 78fdbd00fad7b578758a7df6ceb346f53171cbb3 9db21cda339202c2b31bd7fa5cf6460060c4f7ea 0ab65e4e067c1de493a4a7a71e27ac0921c96c66 0b487e344992e37c49b01912b8ab5f446dd47ec3 2f61bfdbe221889e3f2581bcab01b54a28b6d5b7 4112459399ecf5dad364e87aa7758591d4742667
["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Mathematics","Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"]
1995 2008 2006 1991 2003 1983 2011 2012 2013 2002 1996 1988 1998 2018 2012 2016 2008 2013 1991 2012 2018 1997 2008 2010 2010 2003 2007 2017 2008 2008 2004 2002 2011 2015 2010 2010 2006 2007 1998 2017 2017 1998 2019 2009 2015
PSATS ICIP (1) HotStorage ICISS HPCC/CSS/ICESS
https://api.semanticscholar.org/5689974d3b15e281eb40f61b1400e23a6e624255 https://api.semanticscholar.org/9b9ef204c0bed114dbadf34f81619b7ca1234889 https://api.semanticscholar.org/927d337d4c4d32cff0aec57dce2259d3fa6c00b1 https://api.semanticscholar.org/c919a0ae9ca5ff3d48cdd9ff8801a1969cf047aa https://api.semanticscholar.org/f9fa8e5800a43909c0cb835107b257f2960abefe https://api.semanticscholar.org/ae0f6c707b930e5ad40582a7f1b6b40925eab0f7 https://api.semanticscholar.org/710ca0800385389754567229058928fafff028e4 https://api.semanticscholar.org/d6764782a06549bc824365b5c025909b04d14204 https://api.semanticscholar.org/5c5e54f4aee9e51fde0c3cdbc4de3c36117e8a6e https://api.semanticscholar.org/8a6295b3a995f98ce2d6c0610b7faf8a17a51889 https://api.semanticscholar.org/47be952f61481b3a7867faee7756af9c61e4cb9b https://api.semanticscholar.org/6b954d242e8206d2d7f462f928cb587a044448f7 https://api.semanticscholar.org/31c34aa1978f4dbc46af0bae0054f11f3260876f https://api.semanticscholar.org/4d78b527e5e528ec483945882d9534c57b4f5d1b https://api.semanticscholar.org/e4c36ca6048f09bd18fc9f4519cbf445e64861ec https://api.semanticscholar.org/1f82167e33613159e7fbfe436abe437e603dcfaa https://api.semanticscholar.org/90cec780a7edfd3f7d2c2607a09e3c73d38bd0fb https://api.semanticscholar.org/5c5e54f4aee9e51fde0c3cdbc4de3c36117e8a6e https://api.semanticscholar.org/ed683a992b47af7e262ad495e29baaa2b2b0cc9d https://api.semanticscholar.org/d6764782a06549bc824365b5c025909b04d14204 https://api.semanticscholar.org/794a7c05cf760dc33c6627aa7c0da6b7108af72b https://api.semanticscholar.org/5ac47c33c41e0e5f3a97356c5451d42c63921619 https://api.semanticscholar.org/bc8a241911e074205da7e385e47d052770f2ac45 https://api.semanticscholar.org/60df24f79097701e34f5af83e9ea7f293a2b3c83 https://api.semanticscholar.org/aa772c4fc907a9493b2c3b2556ef102d7846c5e6 https://api.semanticscholar.org/8b6c431937083c19bf2e1d4c08d52ea4bd14b9b8 https://api.semanticscholar.org/aa848e3281a910e92d2d3ef4a32d50a92605fbfc https://api.semanticscholar.org/d33757bea43a6f55d03fdd4202d5c8dd237d09c2 https://api.semanticscholar.org/da532212758e4d0ceb73b26453f1c398391928bd https://api.semanticscholar.org/c92d74da9bbbe1e98ad387228205dcf7a3b67998 https://api.semanticscholar.org/7937c7ec81e34dcc2ebde26aacbfd78df4e898ae https://api.semanticscholar.org/1b63a78e28f5b53007bfc7a87bda158ce48de043 https://api.semanticscholar.org/998876d1b62eb51c874e58beb25637e5a45d1a2a https://api.semanticscholar.org/e92a8b56620aa1fe7df55b3a113696afd3e616a0 https://api.semanticscholar.org/749f9d76a6d4890bdb44a12574c72901b94340a4 https://api.semanticscholar.org/2ac284a213e55121fffc1d04499aa53ad0792c5c https://api.semanticscholar.org/7cf99f6785b13931e233c692cb2ab87a59d97a9c https://api.semanticscholar.org/053be0bf2c952466db7f8158ee9232c2ce4e5d35 https://api.semanticscholar.org/01c1e773fff41dd2d24dddedab3f3608f7df82d6 https://api.semanticscholar.org/c1fb0de8b3264799df6c8b9e07703e414e5fe3eb https://api.semanticscholar.org/78fdbd00fad7b578758a7df6ceb346f53171cbb3 https://api.semanticscholar.org/9db21cda339202c2b31bd7fa5cf6460060c4f7ea https://api.semanticscholar.org/0ab65e4e067c1de493a4a7a71e27ac0921c96c66 https://api.semanticscholar.org/0b487e344992e37c49b01912b8ab5f446dd47ec3 https://api.semanticscholar.org/2f61bfdbe221889e3f2581bcab01b54a28b6d5b7 https://api.semanticscholar.org/4112459399ecf5dad364e87aa7758591d4742667