In the receiver side , with the help of the side information , the compressed image can be decoded and reconstructed . Simulation results show that the proposed scheme is able to provide much higher compression performance compared with the JPEG method . In this paper , we propose to extend the JPEG file for-mat , as the most popular image format , in a backward compatible manner to represent a stereo pair and additional depth data . We propose an architecture to achieve such backward compatibility with JPEG . The coding efficiency of a simple implementation of the proposed architecture is compared to the state of the art stereoscopic 3D image compression and storage formats . This chapter introduces the Joint Photographic Experts Group ( JPEG ) , Motion JPEG ( M-JPEG ) , and Moving Picture Experts Group ( MPEG ) video compression techniques . Most data compression techniques , including run-length encoding ( RLE ) and LempeI-Ziv- Welch ( LZW ) , accomplish compression by taking advantage of repeated substrings ; performance is highly dependent upon the data being compressed . In this paper , we propose the efficient method to be able to decipher hundreds of medical images . On exploring the Motion-JPEG ( M-JPEG ) , this paper showed the possibilities for the management of medical images with the design of each JPEG file format and the improvement to diagnose the moving pictures . The goal is to produce a high quality image while accurately meeting a filesize target and keeping computational complexity — especially the memory usage and number of passes at the input image — low . Building upon the work of He and Mitra , the JPEG transcoder exploits a linear relationship between the number of zero-valued quantized DCT coefficients and the bitrate . Using this relationship and a histogram of coefficients , it is possible to determine an effective way to scale the quantization tables of an image to approach a target filesize . As the image is being transcoded , an intra-image process makes minor corrections , saving more bits as needed throughout the transcoding of the image . This intra-image process decrements specific coefficients , minimizing the change in value ( and hence image quality ) while maximizing the savings in bitrate . The result is a fast JPEG transcoder that reliably achieves a target filesize and preserves as much image quality as possible . The proposed transcoder and several variations were tested on a set of twentynine images that gave a fair representation of typical JPEG photos . The related works in this field have focused more on raster images than on compressed images . Many existing JPEG image encryption schemes are not quite well compatible with the JPEG standard , or the file size of an encrypted JPEG image is apparently increased . In this paper , a novel bitstream-based JPEG image encryption method is presented . As a result , there is an imprecise and ineffective user experience for searching multimedia content . This paper presents a new development underway from a joint effort between International Organization for Standardization (ISO)/International Electrotechnial Commission ( IEC ) Subcommittee ( SC ) 29 Working Group ( WG ) 11 MPEG ( Moving Picture Experts Group ) and WG1 JPEG ( Joint Picture Experts Group ) for a universal standard query format called MPEG-7 Query Format ( MP7QF ) as a means to enable a good user experience for consumers searching multimedia content . It also provides the industry with a unified way to accept and respond to user queries . By using the IDWT , the original image is retrieved without any loss of information in the image . To compare the measure of compression MSE , PSNR and SNR values , ROI of the CT images are compressed by using standard JPEG and DWT methods . Image compression using JPEG , DWT and Modified DWT has been developed to find optimum method for transmitting medical images through the Internet with less storage space and low bandwidth requirements . In this article , we present a new algorithm for JPEG-compressed images which is applicable to mobile platforms . This algorithm embeds sensitive information into quantized discrete cosine transform coefficients obtained from the cover JPEG . These coefficients are rearranged based on certain statistical properties and the inherent processing and memory constraints of mobile devices . The 300 24-bit TIFF images required over 200 MBytes of disk space for their storage . I considered both JPEG and LZW compression techniques to reduce their storage requirement ( and consequently the amount of data that had to move through the network ) , but rejected those “ solutions ” because of the time it takes to decompress each image when a student accesses it . On the other hand , all but one of the workstations from which students access the database are limited to 12-bit color , so I reduced the color depth to 12 , using a maximum palette of four thousand colors per image instead of 16 million . Since the mapping is non-linear , look-up tables are transmitted to the decoder as part of the overhead information . Experimental results show that the compression in bit per pixel ( bpp ) of the proposed algorithm does not only exceed wavelet-based SPHIT and JPEG 2000 but also it exceeds the state-of-the-art context-based JPEG-LS . Malware creators are ahead of the malware detectors . In this paper , we present eight techniques of hiding a malicious Android application inside images ( PNG/JPEG ) by using methods such as Concatenation , Obfuscation , Cryptography , and Steganography separately and in conjunction . The image containing the malicious application is stored in the resources of another Android application . However , high compression rates come with visual artifacts that degrade the image quality and thus user experience . Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems . Similar to training for image deblurring , we generate JPEG compressed image patches from original non - compressed ones on the fly during training . This can be easily done using JPEG compression function by varying the quality factor ( QF ) of interest . In Table [ reference ] , we list the performance of our model and compare to the state - of - the - art methods . The output filter flow maps indicate from the colorful edges how the pixels are warped from the neighborhood in the input image . This also clearly shows where the JPEG image degrades most , e.g. , the large sky region is quantized by JPEG compression . Though CNN makes the block effect smooth to some extent , our PFF produces the best visual quality , smoothing the block artifact while maintaining both high - and low - frequency details . subsection : JPEG deblocking Lossy compression , such as JPEG , introduces complex compression artifacts , particularly the blocking artifacts , ringing effects and blurring . In this section , we carry out deblocking experiments to recover high quality images from their JPEG compression . As in other compression artifacts reduction methods , standard JPEG compression schemes of JPEG quality settings and in MATLAB JPEG encoder are used . The LIVE1 dataset is used for evaluation , and we have compared our method with AR - CNN , SA - DCT and deeper SRCNN . As an example , at a low level of compression ( close to lossless compression ) , we could start by reducing pixel precision , e.g. from 8 bits to 7 bits . Then , as in JPEG , we could express a local 8x8 neighborhood in a discrete cosine transform basis and store only the most significant components . This way , instead of introducing quantization artifacts in the image that would appear if we kept decreasing pixel precision , we preserve higher level structures but to a lower level of precision . subsubsection : Implementation Details We enhance the diversity of raw training data on account of their limited variation patterns , using five steps : rotation , uniform scaling , translation , horizontal flip , and JPEG compression . In particular , for each training face , we firstly perform multiple rotations , and attain a tight face bounding box covering the ground truth locations of landmarks of each rotated result respectively . A compression scheme for calibrated images depicting a static scene from arbitrary viewpoints is presented . 3D scene geometry is reconstructed , and view-dependent texture maps are generated from all images . The experimental results on various non-trivial 3D textures and polygonal models show that high compression rates are achieved with few visual artifacts in the rendered image and a small impact on rendering time . The simplicity of our compression-based scheme will make it possible to implement practical 3D texture mapping in software/hardware rendering systems including the real-time 3D graphics APIs like OpenGL and Direct3D . Using steganography alone with simple LSB has a potential problem that the secret message is easily detectable from the histogram analysis method . To improve the security as well as the image embedding capacity indirectly , a compression based scheme is introduced . Various tests have been done to check the storage capacity and message distribution . In its bibliographic search this bachelor 's degree dissertation presents the issue of the educational process and the use of interactive and multimedia aids at secondary schools in the Zlin Region . The theoretical part contains a clear overview of modern teaching methods , the use of media in teaching , way of transferring information by technical means , multimedia presentations and characteristics of the institutions ' activities as well as compression algorithms that are used in practice and new trends in compression algorithm methods . The practical part focuses on statistical evaluation of the information and data amassed and processed in connection with the issue of the use of interactive and multimedia aids at secondary schools in the Zlin Region as well as a visualisation of the data using Borland Delphi 3D programme log with the basic manual of the OPENGL programming language and a list of new trends in the area . In this work we propose the use of the discrete wavelet transform for the detection of multiple encoding rhythms that appear , for example , in spatio-temporal patterns generated by neuronal activity in a set of coupled neurons . The method here presented allows a quantitative characterization of spatio-temporal patterns and is based on the behavior of a compression-like scheme . The wavelet-based method is faster than the two-dimensional spectral methods for finding different rhythms on spatio-temporal patterns , as it has a computational complexity O ( width ×height ) for each 2D-frame of the spatio-temporal pattern . Theproblem also occurs in video compression , wherethecorrelation between successive images isexploited atthedecoder onlyinorder toreduce the complexity attheencoder side . Forthis video application , thecompression scheme operates atacorner point oftheSW region . Onesource iscompressed whiletheother one , called thesideinformation ( SI ) , is sentdirectly tothedecoder . The first one simply consists in downsizing the input images , and upscaling the resulting matches accordingly . The second option is the compression scheme proposed in Section [ reference ] . We evaluate both schemes jointly by varying the input image size ( expressed as a fraction of the original resolution ) and the size of the prototype dictionary ( i.e . parameter of k - means in Section [ reference ] ) . Morphological segmentation has been proposed as an attractive alternative to transform-based compression for interframe coding of digital video . Being a spatial approach , segmentation based coding eliminates the artefacts commonly associated with transform coding , such as ringing around sharp edges . One disadvantage of this method is that it can introduce spurious edges in the reconstructed video sequence , associated with the boundaries of the transmitted regions . Numerical experiments investigating the effectiveness of compressed sensing using real datasets show its potential for minimizing sensing and overall energy costs in Wireless Sensor Networks based gas emission monitoring system . Results shown that , for gas monitoring , compressed sensing can provide greater energy efficiency than transform coding , and model based adaptive sensing in Wireless Sensor Networks . Three methods have been discussed in this thesis , and our proposed average unpredictability scheme was selected for implementation mainly because of its low computational weight and relatively good quality . AAC employs transform coding scheme with MDCT as its transform engine . In a traditional encoder , this is the second transform process performed besides DFT in psychoacoustics module . In a transform coding framework , we compare the optimal causal approach ( LDU , Lower-Diagonal-Upper ) to the optimal unitary approach ( Karhunen-Loeve Transform , KLT ) . The criterion of merit used for this comparison is the coding gain , defined for a transformation T as the ratio of the average distortion obtained with the identity transformation over the average distortion obtained with T. Both transforms are known to yield the same gain when they are computed on the signal covariance matrix R. The purpose of this paper is to compare the behavior of these two transformations when the ideal transform coding scheme gets perturbed , that is , when only an estimate R + ΔR of R is known . In this case , not only the transformation itself will be perturbated , but also the bit allocation mechanism . With such a pattern-driven model , efficient compression can be achieved by designing and developing novel ways of modeling and encoding disparate low-level visual patterns . For a given image , disparate low-level visual patterns are automatically separated , modeled , and encoded by : ( 1 ) A geometric filter , captures and models the predominant structural lines/curves ( SD ) , ( 2 ) A linear filter , breaks various image regions into variable size triangular tiles and applies linear regression techniques to efficiently model tiles with HP , and ( 3 ) non-linear filters , model CP content such as cluttered background and textures , using advanced learning and transform based coding techniques . After all the filtering pipelines , adaptive entropy encoding schemes are employed to further reduce the bit cost by eliminating the statistical redundancies exhibited by all the features extracted and modeling parameters . Yet , existing hybrid coders are hampered by the lack of integrative interplay between both techniques . We outline our ideas how to tightly integrate transform-based coding and parametric coding to obtain an enhanced perceptual quality and scalability . Also , we provide listening test results which demonstrate the benefits of our hybrid coder design . To deal with the high performance video compression efficiency and real-time playback solutions , a H.264/AVC encoder IP core based on Field Programmable Gate Array(FPGA)is implemented , which contains all the coding process , including both intra and inter prediction , transform-based coding , etc.,and a new type of parallel processing architecture is proposed . To compensate the low-frequency of FPGA chip , high degree of pipeline structure , double-buffers and multi-time-domain are used . For low bit rates the visual quality of coded image sequences is more important than objective PSNR error measures . Especially at very low bit rates standard transform coding based methods like H.261 and MPEG suffer from annoying blocking artifacts . Wavelet-based coding schemes using overlapping basis functions avoid these blocking artifacts . We present a new image compression scheme for hyperspectral images based on the newly-emerged matrix/tensor completion theory . Unlike typical transform-coding based methods , the proposed approach does not require any transform to be performed by the imaging sensor when doing on-board compression . Only a small set of pixels on a sparse set of locations on the imaging sensor needs to be captured and transmitted for each image . The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches . While , so far , research has mainly focused on architecture and model improvements , in this work we explore content adaptive optimization . Trellis-coded quantization ( TCQ ) is incorporated into a transform coding structure for encoding monochrome images . Both fixed-rate and entropy-constrained designs are considered . Trellis coded quantization ( TCQ ) is incorporated into a transform coding structure for encoding monochrome and color images at 1 bit/pixel . Both fixed-rate and entropy-constrained designs are considered . Trellis coded quantization ( TCQ ) is an efficient technique for encoding memoryless sources . Furthermore TCQ can be incorporated into a transform coding structure ( such as the discrete cosine transform ) for encoding monochrome and color images with fixed rate or entropy-constrained schemes . In all these cases an expanded codebook is partitioned into subsets used to label the branches of an appropriate graph ( trellis ) . In these systems , a quantized and coded version of a spatial unitary transform of an image is transmitted over a channel , rather than an image itself . In this paper the transform-coding concept has been applied to the coding of color images represented by three primary color planes of data . The principles of spatial transform coding are reviewed and the merits of various methods of color-image representation are discussed . A new progressive image transmission technique using adaptive block truncation coding is introduced in this paper . The adaptive block truncation coding was proposed as a non- transform coding mode for edge blocks in digital video sequences . By adapting more sophisticated just-noticeable-difference model of the human visual system , we can get an improved adaptive block truncation coding than the original work . We presented first results of the compensation step , here we will show results of the complete coding scheme for rectangular frames . As the insertion of the transform/coding part raised the problem of which type of transformation should be employed to code the error image efficiently and without disturbing the gains of the compensation step , we here compare DCT and DWT ( discrete wavelet transform ) with different filter types , to find out which one is most useful to apply with mesh-based motion compensation . Abstract . The new video coding standard , high-efficiency video coding , adopts a quadtree structure to provide variable transform sizes in the transform coding process . The heuristic examination of transform unit ( TU ) modes substantially increases the computational complexity , compared to previous video coding standards . This work investigates the tradeoff between the computational power spent in performing the transform operations for HEVC compression and the corresponding video quality as a function of a number of coding configuration parameters . Results provides a practical method to determine the throughput needed by the transform coding subsystem as well as the optimal configuration of the considered coding parameters for each desired complexity-quality tradeoff , showing that with small quality reduction large computational power savings are possible . This paper aims to implement compression of ECG signal using wavelet-based progressive coding such as set partitioning in hierarchical trees . Further improvement in the compression ratio is possible with modified run-length encoding . The experimentation performed with the MIT-BIH arrhythmia database shows compression ratio of 16:1 and average mean square difference percentage of 0.58 using bior4.4 wavelet . Run-length encoding is a popular compression scheme which is used extensively to compress the attribute values in column stores . Out of order insertion of tuples potentially degrades the compression achieved using run-length encoding and consequently , the performance of reads . In this paper , an integrated image compression and encryption technique using run length encoding scheme and henon chaotic map is presented . Run length encoding scheme is common scheme and a natural choice for image compression . Run length encoding generates ( value , count ) pairs such that the value is repeated ‘ count ’ number of times . Each of these encoding methods is appropriate for different query types . The bit partitions can also be compressed using a version of the run length encoding scheme . Efficient operators on compressed bit vectors have been developed and form the basis of a query language . A significant improvement in compression ratio for almost any kind of data can be achieved by the proposed scheme . All the limitations and problems in the original run length encoding scheme have been highlighted and discussed in detail in this research paper . A proposed solution has been suggested and performed for each problem to achieve intelligent and efficient coding . We can ignore ASCII arts in text data or replace them with other strings by ASCII art extraction methods , which detect areas of ASCII arts in a given text data . Our research group and another research group independently proposed two different ASCII art extraction methods , which are a run-length encoding based method and a byte pattern based method respectively . Both of the methods use text classifiers constructed by machine learning algorithms , but they use different attributes of text . In this paper , we compare the two methods by ASCII art extraction experiments where training text and testing text are in English and Japanese . Our experimental results show that the two methods are competitive if training text and testing text are in a same set of languages , but the run-length encoding based method works better than the byte pattern based method if training text and testing text are in different sets of languages . This paper presented a new run-length encoding based method of extracting features from multi-target image . The algorithm uses linked list structure to store data . The algorithm of Raster Detection Plus Vector Calculation is one of the high effective method for processing of GIS′s vector intersection . Starting from the view of economizing in memory and increasing operation speed , the authors improved the rasterization method by thoughts of RLE ( Running Length Encoding ) , that makes it practicable to apply algorithm on large-scale vector datum . This paper not only describes the principle of the method , but also introduces the steps in detail , and demonstrates the preliminary experiment results . When encoding , the difference between each image block and its matching codeword is first computed to obtain the difference image , and then the sign bits of the pixel difference is imposed the running length coding on and attached after the codeword index . When decoding , this proposed method restores the compressed image according to the codeword index , performs the running length encoding to deal with decode the attached information , uses the window of 3 3 size to tackle the adaptive difference compensation and derive the final decoding image . The experiment results reveal that this proposed method can improve the encoding speed and image restoration performance against the normal vector quantization . This paper presents a development of new size-controlled compression algorithm for Electrocardiogram signal ( ECG ) . Discrete Wavelet Transform ( DWT ) method , Bit-Field Preserving ( BFP ) and Running Length Encoding ( RLE ) are selected as compression tools in this work . Even though DWT-BFP-RLE is a lossy compression method , it has shown a potential in preserving the critical ( diagnostic ) part of the signal . Considering the deficiency of direct-encoding raster data in computation efficiency and storage capacity , in this paper , a new run-length encoding data structure is proposed , which is suitable for algebraic operations and could optimize the algebraic operations that are based direct-encoding raster data . In this paper , the implementation of ldquointersectionrdquo operation based on this data structure is introduced ; besides , all kinds of algebraic operations on the run-lengthpsilas attributes are realized in the process of implementation . Considering that the direct encoding raster data has a deficiency on the computational efficiency and storage capacity , a new run-length encoding data structure which is suitable to the algebraic operations and can optimize the algebraic operations based on direct encoding raster data is proposed in this paper . The implementation of intersection operation based on this data structure is discussed and the algebraic operations of the attributes are described . We show that particular choices of the AN-code parameter lead to a moderate loss of performance . We provide evaluation for two proposed techniques , namely AN-encoded Null Suppression and AN-encoded Run Length Encoding . This paper presents a test compression method that effectively derives the capability of a run-length based encoding . The method employs two techniques : scan polarity adjustment and pinpoint test relaxation . A prediction error calculating unit 111 , calculates a prediction error for each input data , and the prediction error encoding unit 112 encodes the value of the prediction error generating a prediction error code , and the run length counting section 121 is a prediction error is a run run-length encoding unit 122 counts the length , and , when the value of the predictive error variation , and encoding the coefficient run length is generated for the run-length code , and the code combining unit 131 , the run-length code generating a combined code by combining the prediction error the sign of the prediction error corresponding to the prediction error determination section 141 is , select the combination code to the prediction error as output code if the value is the value of the prediction error specific and , selecting a prediction error code to the prediction error when the other value as an output code , and the code output unit 151 outputs the selected output code . Lattice Boltzmann method is widely used in the area of CFD , whereas it is both computationally demanding and memory intensive . In this paper , a mesh compression algorithm is proposed , the situation of excessive consumption of space and computation is improved;and the process of migration and collision are consolidated , the memory I/O is reduced and computational speed is much better;the algorithm has good scalability . The results indicate that optimization memory demanding is improved , performance is increased by more than 50 % . Presents an algorithm that uses partitioning and gluing to compress large triangular meshes which are too complex to fit in main memory . The algorithm is based largely on the existing mesh compression algorithms , most of which require an ' in-core ' representation of the input mesh . Our solution is to partition the mesh into smaller submeshes and compress these submeshes separately using existing mesh compression techniques . Laplacian mesh compression , also known as high‐pass mesh coding , is a popular technique for efficiently storing both static and dynamic triangle meshes that gained further recognition with the advent of perceptual mesh distortion evaluation metrics . Currently , the usual rule of thumb that drives the decision for a mesh compression algorithm is whether or not accuracy in absolute scale is required : Laplacian mesh encoding is chosen when perceptual quality is the main objective , while other techniques provide better results in terms of mechanistic error measures such as mean squared error . Vertex coordinates in VRML scenes are represented by 6 decimal digits or even less . Mesh compression schemes may quantize vertex coordinates to even below precision of 4 decimal digits . The second contribution of this paper is a general technique for reducing processing time of watermark ( label ) extraction satisfying impatient users and enhancing robustness with respect to affine transformations and , in particular , vertex randomization attacks . This has resulted in image compression being an important issue in reducing the cost of data storage and transmission time . Mesh based compression is one among them . Wavelet transform is a powerful mathematical tool with many unique qualities that are useful for image compression and processing applications . The representation of 3D video is often based on dynamic 3D mesh model , which is reconstructed from multi-view video , plus surrounding texture information for rendering , so that arbitrary novel views can be synthesized accordingly . However , the dynamic 3D mesh model herein is not time-consistent , resulting in a difficulty in applying traditional mesh compression tools efficiently ( e.g. , MPEG-4 AFX 3DMC ) . In this paper , we modify the 3DMC algorithm for the coding and transmission of 3D video , taking its advantage of high coding efficiency for edge topologies and enhancing it with 3D motion estimation of vertices between two time-successive mesh models . This paper describes CoolPression , a hybrid hardware-based significance compression technique for reducing energy in caches . The scheme exploits data compression opportunities at bit granularity by employing two compression schemes : a significance compression scheme that counts leading ones and zeros , and a dynamic zero compression technique . Based on actual data , the more energy-efficient scheme is selected dynamically to minimize bitline drives needed for each cache access . As Recurrent Neural Networks is good at processing and making predictions based on series of data , we propose a Long-Short Term Memory network based model to select compression algorithm for input data blocks adaptively . Given a typical database benchmark , we implemented our model to formulate compression strategies for each data block and managed to reduce at most 15 % storage size than using a single compression algorithm scheme . However , high compression rates come with visual artifacts that degrade the image quality and thus user experience . Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems .
In the receiver side , with the help of the side information , the compressed image can be decoded and reconstructed . Simulation results show that the proposed scheme is able to provide much higher compression performance compared with the JPEG method .
In this paper , we propose to extend the JPEG file for-mat , as the most popular image format , in a backward compatible manner to represent a stereo pair and additional depth data . We propose an architecture to achieve such backward compatibility with JPEG . The coding efficiency of a simple implementation of the proposed architecture is compared to the state of the art stereoscopic 3D image compression and storage formats .
This chapter introduces the Joint Photographic Experts Group ( JPEG ) , Motion JPEG ( M-JPEG ) , and Moving Picture Experts Group ( MPEG ) video compression techniques . Most data compression techniques , including run-length encoding ( RLE ) and LempeI-Ziv- Welch ( LZW ) , accomplish compression by taking advantage of repeated substrings ; performance is highly dependent upon the data being compressed .
In this paper , we propose the efficient method to be able to decipher hundreds of medical images . On exploring the Motion-JPEG ( M-JPEG ) , this paper showed the possibilities for the management of medical images with the design of each JPEG file format and the improvement to diagnose the moving pictures .
The goal is to produce a high quality image while accurately meeting a filesize target and keeping computational complexity — especially the memory usage and number of passes at the input image — low . Building upon the work of He and Mitra , the JPEG transcoder exploits a linear relationship between the number of zero-valued quantized DCT coefficients and the bitrate . Using this relationship and a histogram of coefficients , it is possible to determine an effective way to scale the quantization tables of an image to approach a target filesize . As the image is being transcoded , an intra-image process makes minor corrections , saving more bits as needed throughout the transcoding of the image . This intra-image process decrements specific coefficients , minimizing the change in value ( and hence image quality ) while maximizing the savings in bitrate . The result is a fast JPEG transcoder that reliably achieves a target filesize and preserves as much image quality as possible . The proposed transcoder and several variations were tested on a set of twentynine images that gave a fair representation of typical JPEG photos .
The related works in this field have focused more on raster images than on compressed images . Many existing JPEG image encryption schemes are not quite well compatible with the JPEG standard , or the file size of an encrypted JPEG image is apparently increased . In this paper , a novel bitstream-based JPEG image encryption method is presented .
As a result , there is an imprecise and ineffective user experience for searching multimedia content . This paper presents a new development underway from a joint effort between International Organization for Standardization (ISO)/International Electrotechnial Commission ( IEC ) Subcommittee ( SC ) 29 Working Group ( WG ) 11 MPEG ( Moving Picture Experts Group ) and WG1 JPEG ( Joint Picture Experts Group ) for a universal standard query format called MPEG-7 Query Format ( MP7QF ) as a means to enable a good user experience for consumers searching multimedia content . It also provides the industry with a unified way to accept and respond to user queries .
By using the IDWT , the original image is retrieved without any loss of information in the image . To compare the measure of compression MSE , PSNR and SNR values , ROI of the CT images are compressed by using standard JPEG and DWT methods . Image compression using JPEG , DWT and Modified DWT has been developed to find optimum method for transmitting medical images through the Internet with less storage space and low bandwidth requirements .
In this article , we present a new algorithm for JPEG-compressed images which is applicable to mobile platforms . This algorithm embeds sensitive information into quantized discrete cosine transform coefficients obtained from the cover JPEG . These coefficients are rearranged based on certain statistical properties and the inherent processing and memory constraints of mobile devices .
The 300 24-bit TIFF images required over 200 MBytes of disk space for their storage . I considered both JPEG and LZW compression techniques to reduce their storage requirement ( and consequently the amount of data that had to move through the network ) , but rejected those “ solutions ” because of the time it takes to decompress each image when a student accesses it . On the other hand , all but one of the workstations from which students access the database are limited to 12-bit color , so I reduced the color depth to 12 , using a maximum palette of four thousand colors per image instead of 16 million .
Since the mapping is non-linear , look-up tables are transmitted to the decoder as part of the overhead information . Experimental results show that the compression in bit per pixel ( bpp ) of the proposed algorithm does not only exceed wavelet-based SPHIT and JPEG 2000 but also it exceeds the state-of-the-art context-based JPEG-LS .
Malware creators are ahead of the malware detectors . In this paper , we present eight techniques of hiding a malicious Android application inside images ( PNG/JPEG ) by using methods such as Concatenation , Obfuscation , Cryptography , and Steganography separately and in conjunction . The image containing the malicious application is stored in the resources of another Android application .
However , high compression rates come with visual artifacts that degrade the image quality and thus user experience . Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems .
Similar to training for image deblurring , we generate JPEG compressed image patches from original non - compressed ones on the fly during training . This can be easily done using JPEG compression function by varying the quality factor ( QF ) of interest . In Table [ reference ] , we list the performance of our model and compare to the state - of - the - art methods .
The output filter flow maps indicate from the colorful edges how the pixels are warped from the neighborhood in the input image . This also clearly shows where the JPEG image degrades most , e.g. , the large sky region is quantized by JPEG compression . Though CNN makes the block effect smooth to some extent , our PFF produces the best visual quality , smoothing the block artifact while maintaining both high - and low - frequency details .
subsection : JPEG deblocking Lossy compression , such as JPEG , introduces complex compression artifacts , particularly the blocking artifacts , ringing effects and blurring . In this section , we carry out deblocking experiments to recover high quality images from their JPEG compression . As in other compression artifacts reduction methods , standard JPEG compression schemes of JPEG quality settings and in MATLAB JPEG encoder are used . The LIVE1 dataset is used for evaluation , and we have compared our method with AR - CNN , SA - DCT and deeper SRCNN .
As an example , at a low level of compression ( close to lossless compression ) , we could start by reducing pixel precision , e.g. from 8 bits to 7 bits . Then , as in JPEG , we could express a local 8x8 neighborhood in a discrete cosine transform basis and store only the most significant components . This way , instead of introducing quantization artifacts in the image that would appear if we kept decreasing pixel precision , we preserve higher level structures but to a lower level of precision .
subsubsection : Implementation Details We enhance the diversity of raw training data on account of their limited variation patterns , using five steps : rotation , uniform scaling , translation , horizontal flip , and JPEG compression . In particular , for each training face , we firstly perform multiple rotations , and attain a tight face bounding box covering the ground truth locations of landmarks of each rotated result respectively .
A compression scheme for calibrated images depicting a static scene from arbitrary viewpoints is presented . 3D scene geometry is reconstructed , and view-dependent texture maps are generated from all images .
The experimental results on various non-trivial 3D textures and polygonal models show that high compression rates are achieved with few visual artifacts in the rendered image and a small impact on rendering time . The simplicity of our compression-based scheme will make it possible to implement practical 3D texture mapping in software/hardware rendering systems including the real-time 3D graphics APIs like OpenGL and Direct3D .
Using steganography alone with simple LSB has a potential problem that the secret message is easily detectable from the histogram analysis method . To improve the security as well as the image embedding capacity indirectly , a compression based scheme is introduced . Various tests have been done to check the storage capacity and message distribution .
In its bibliographic search this bachelor 's degree dissertation presents the issue of the educational process and the use of interactive and multimedia aids at secondary schools in the Zlin Region . The theoretical part contains a clear overview of modern teaching methods , the use of media in teaching , way of transferring information by technical means , multimedia presentations and characteristics of the institutions ' activities as well as compression algorithms that are used in practice and new trends in compression algorithm methods . The practical part focuses on statistical evaluation of the information and data amassed and processed in connection with the issue of the use of interactive and multimedia aids at secondary schools in the Zlin Region as well as a visualisation of the data using Borland Delphi 3D programme log with the basic manual of the OPENGL programming language and a list of new trends in the area .
In this work we propose the use of the discrete wavelet transform for the detection of multiple encoding rhythms that appear , for example , in spatio-temporal patterns generated by neuronal activity in a set of coupled neurons . The method here presented allows a quantitative characterization of spatio-temporal patterns and is based on the behavior of a compression-like scheme . The wavelet-based method is faster than the two-dimensional spectral methods for finding different rhythms on spatio-temporal patterns , as it has a computational complexity O ( width ×height ) for each 2D-frame of the spatio-temporal pattern .
Theproblem also occurs in video compression , wherethecorrelation between successive images isexploited atthedecoder onlyinorder toreduce the complexity attheencoder side . Forthis video application , thecompression scheme operates atacorner point oftheSW region . Onesource iscompressed whiletheother one , called thesideinformation ( SI ) , is sentdirectly tothedecoder .
The first one simply consists in downsizing the input images , and upscaling the resulting matches accordingly . The second option is the compression scheme proposed in Section [ reference ] . We evaluate both schemes jointly by varying the input image size ( expressed as a fraction of the original resolution ) and the size of the prototype dictionary ( i.e . parameter of k - means in Section [ reference ] ) .
Morphological segmentation has been proposed as an attractive alternative to transform-based compression for interframe coding of digital video . Being a spatial approach , segmentation based coding eliminates the artefacts commonly associated with transform coding , such as ringing around sharp edges . One disadvantage of this method is that it can introduce spurious edges in the reconstructed video sequence , associated with the boundaries of the transmitted regions .
Numerical experiments investigating the effectiveness of compressed sensing using real datasets show its potential for minimizing sensing and overall energy costs in Wireless Sensor Networks based gas emission monitoring system . Results shown that , for gas monitoring , compressed sensing can provide greater energy efficiency than transform coding , and model based adaptive sensing in Wireless Sensor Networks .
Three methods have been discussed in this thesis , and our proposed average unpredictability scheme was selected for implementation mainly because of its low computational weight and relatively good quality . AAC employs transform coding scheme with MDCT as its transform engine . In a traditional encoder , this is the second transform process performed besides DFT in psychoacoustics module .
In a transform coding framework , we compare the optimal causal approach ( LDU , Lower-Diagonal-Upper ) to the optimal unitary approach ( Karhunen-Loeve Transform , KLT ) . The criterion of merit used for this comparison is the coding gain , defined for a transformation T as the ratio of the average distortion obtained with the identity transformation over the average distortion obtained with T. Both transforms are known to yield the same gain when they are computed on the signal covariance matrix R. The purpose of this paper is to compare the behavior of these two transformations when the ideal transform coding scheme gets perturbed , that is , when only an estimate R + ΔR of R is known . In this case , not only the transformation itself will be perturbated , but also the bit allocation mechanism .
With such a pattern-driven model , efficient compression can be achieved by designing and developing novel ways of modeling and encoding disparate low-level visual patterns . For a given image , disparate low-level visual patterns are automatically separated , modeled , and encoded by : ( 1 ) A geometric filter , captures and models the predominant structural lines/curves ( SD ) , ( 2 ) A linear filter , breaks various image regions into variable size triangular tiles and applies linear regression techniques to efficiently model tiles with HP , and ( 3 ) non-linear filters , model CP content such as cluttered background and textures , using advanced learning and transform based coding techniques . After all the filtering pipelines , adaptive entropy encoding schemes are employed to further reduce the bit cost by eliminating the statistical redundancies exhibited by all the features extracted and modeling parameters .
Yet , existing hybrid coders are hampered by the lack of integrative interplay between both techniques . We outline our ideas how to tightly integrate transform-based coding and parametric coding to obtain an enhanced perceptual quality and scalability . Also , we provide listening test results which demonstrate the benefits of our hybrid coder design .
To deal with the high performance video compression efficiency and real-time playback solutions , a H.264/AVC encoder IP core based on Field Programmable Gate Array(FPGA)is implemented , which contains all the coding process , including both intra and inter prediction , transform-based coding , etc.,and a new type of parallel processing architecture is proposed . To compensate the low-frequency of FPGA chip , high degree of pipeline structure , double-buffers and multi-time-domain are used .
For low bit rates the visual quality of coded image sequences is more important than objective PSNR error measures . Especially at very low bit rates standard transform coding based methods like H.261 and MPEG suffer from annoying blocking artifacts . Wavelet-based coding schemes using overlapping basis functions avoid these blocking artifacts .
We present a new image compression scheme for hyperspectral images based on the newly-emerged matrix/tensor completion theory . Unlike typical transform-coding based methods , the proposed approach does not require any transform to be performed by the imaging sensor when doing on-board compression . Only a small set of pixels on a sparse set of locations on the imaging sensor needs to be captured and transmitted for each image .
The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches . While , so far , research has mainly focused on architecture and model improvements , in this work we explore content adaptive optimization .
Trellis-coded quantization ( TCQ ) is incorporated into a transform coding structure for encoding monochrome images . Both fixed-rate and entropy-constrained designs are considered .
Trellis coded quantization ( TCQ ) is incorporated into a transform coding structure for encoding monochrome and color images at 1 bit/pixel . Both fixed-rate and entropy-constrained designs are considered .
Trellis coded quantization ( TCQ ) is an efficient technique for encoding memoryless sources . Furthermore TCQ can be incorporated into a transform coding structure ( such as the discrete cosine transform ) for encoding monochrome and color images with fixed rate or entropy-constrained schemes . In all these cases an expanded codebook is partitioned into subsets used to label the branches of an appropriate graph ( trellis ) .
In these systems , a quantized and coded version of a spatial unitary transform of an image is transmitted over a channel , rather than an image itself . In this paper the transform-coding concept has been applied to the coding of color images represented by three primary color planes of data . The principles of spatial transform coding are reviewed and the merits of various methods of color-image representation are discussed .
A new progressive image transmission technique using adaptive block truncation coding is introduced in this paper . The adaptive block truncation coding was proposed as a non- transform coding mode for edge blocks in digital video sequences . By adapting more sophisticated just-noticeable-difference model of the human visual system , we can get an improved adaptive block truncation coding than the original work .
We presented first results of the compensation step , here we will show results of the complete coding scheme for rectangular frames . As the insertion of the transform/coding part raised the problem of which type of transformation should be employed to code the error image efficiently and without disturbing the gains of the compensation step , we here compare DCT and DWT ( discrete wavelet transform ) with different filter types , to find out which one is most useful to apply with mesh-based motion compensation .
Abstract . The new video coding standard , high-efficiency video coding , adopts a quadtree structure to provide variable transform sizes in the transform coding process . The heuristic examination of transform unit ( TU ) modes substantially increases the computational complexity , compared to previous video coding standards .
This work investigates the tradeoff between the computational power spent in performing the transform operations for HEVC compression and the corresponding video quality as a function of a number of coding configuration parameters . Results provides a practical method to determine the throughput needed by the transform coding subsystem as well as the optimal configuration of the considered coding parameters for each desired complexity-quality tradeoff , showing that with small quality reduction large computational power savings are possible .
This paper aims to implement compression of ECG signal using wavelet-based progressive coding such as set partitioning in hierarchical trees . Further improvement in the compression ratio is possible with modified run-length encoding . The experimentation performed with the MIT-BIH arrhythmia database shows compression ratio of 16:1 and average mean square difference percentage of 0.58 using bior4.4 wavelet .
Run-length encoding is a popular compression scheme which is used extensively to compress the attribute values in column stores . Out of order insertion of tuples potentially degrades the compression achieved using run-length encoding and consequently , the performance of reads .
In this paper , an integrated image compression and encryption technique using run length encoding scheme and henon chaotic map is presented . Run length encoding scheme is common scheme and a natural choice for image compression . Run length encoding generates ( value , count ) pairs such that the value is repeated ‘ count ’ number of times .
Each of these encoding methods is appropriate for different query types . The bit partitions can also be compressed using a version of the run length encoding scheme . Efficient operators on compressed bit vectors have been developed and form the basis of a query language .
A significant improvement in compression ratio for almost any kind of data can be achieved by the proposed scheme . All the limitations and problems in the original run length encoding scheme have been highlighted and discussed in detail in this research paper . A proposed solution has been suggested and performed for each problem to achieve intelligent and efficient coding .
We can ignore ASCII arts in text data or replace them with other strings by ASCII art extraction methods , which detect areas of ASCII arts in a given text data . Our research group and another research group independently proposed two different ASCII art extraction methods , which are a run-length encoding based method and a byte pattern based method respectively . Both of the methods use text classifiers constructed by machine learning algorithms , but they use different attributes of text . In this paper , we compare the two methods by ASCII art extraction experiments where training text and testing text are in English and Japanese . Our experimental results show that the two methods are competitive if training text and testing text are in a same set of languages , but the run-length encoding based method works better than the byte pattern based method if training text and testing text are in different sets of languages .
This paper presented a new run-length encoding based method of extracting features from multi-target image . The algorithm uses linked list structure to store data .
The algorithm of Raster Detection Plus Vector Calculation is one of the high effective method for processing of GIS′s vector intersection . Starting from the view of economizing in memory and increasing operation speed , the authors improved the rasterization method by thoughts of RLE ( Running Length Encoding ) , that makes it practicable to apply algorithm on large-scale vector datum . This paper not only describes the principle of the method , but also introduces the steps in detail , and demonstrates the preliminary experiment results .
When encoding , the difference between each image block and its matching codeword is first computed to obtain the difference image , and then the sign bits of the pixel difference is imposed the running length coding on and attached after the codeword index . When decoding , this proposed method restores the compressed image according to the codeword index , performs the running length encoding to deal with decode the attached information , uses the window of 3 3 size to tackle the adaptive difference compensation and derive the final decoding image . The experiment results reveal that this proposed method can improve the encoding speed and image restoration performance against the normal vector quantization .
This paper presents a development of new size-controlled compression algorithm for Electrocardiogram signal ( ECG ) . Discrete Wavelet Transform ( DWT ) method , Bit-Field Preserving ( BFP ) and Running Length Encoding ( RLE ) are selected as compression tools in this work . Even though DWT-BFP-RLE is a lossy compression method , it has shown a potential in preserving the critical ( diagnostic ) part of the signal .
Considering the deficiency of direct-encoding raster data in computation efficiency and storage capacity , in this paper , a new run-length encoding data structure is proposed , which is suitable for algebraic operations and could optimize the algebraic operations that are based direct-encoding raster data . In this paper , the implementation of ldquointersectionrdquo operation based on this data structure is introduced ; besides , all kinds of algebraic operations on the run-lengthpsilas attributes are realized in the process of implementation .
Considering that the direct encoding raster data has a deficiency on the computational efficiency and storage capacity , a new run-length encoding data structure which is suitable to the algebraic operations and can optimize the algebraic operations based on direct encoding raster data is proposed in this paper . The implementation of intersection operation based on this data structure is discussed and the algebraic operations of the attributes are described .
We show that particular choices of the AN-code parameter lead to a moderate loss of performance . We provide evaluation for two proposed techniques , namely AN-encoded Null Suppression and AN-encoded Run Length Encoding .
This paper presents a test compression method that effectively derives the capability of a run-length based encoding . The method employs two techniques : scan polarity adjustment and pinpoint test relaxation .
A prediction error calculating unit 111 , calculates a prediction error for each input data , and the prediction error encoding unit 112 encodes the value of the prediction error generating a prediction error code , and the run length counting section 121 is a prediction error is a run run-length encoding unit 122 counts the length , and , when the value of the predictive error variation , and encoding the coefficient run length is generated for the run-length code , and the code combining unit 131 , the run-length code generating a combined code by combining the prediction error the sign of the prediction error corresponding to the prediction error determination section 141 is , select the combination code to the prediction error as output code if the value is the value of the prediction error specific and , selecting a prediction error code to the prediction error when the other value as an output code , and the code output unit 151 outputs the selected output code .
Lattice Boltzmann method is widely used in the area of CFD , whereas it is both computationally demanding and memory intensive . In this paper , a mesh compression algorithm is proposed , the situation of excessive consumption of space and computation is improved;and the process of migration and collision are consolidated , the memory I/O is reduced and computational speed is much better;the algorithm has good scalability . The results indicate that optimization memory demanding is improved , performance is increased by more than 50 % .
Presents an algorithm that uses partitioning and gluing to compress large triangular meshes which are too complex to fit in main memory . The algorithm is based largely on the existing mesh compression algorithms , most of which require an ' in-core ' representation of the input mesh . Our solution is to partition the mesh into smaller submeshes and compress these submeshes separately using existing mesh compression techniques .
Laplacian mesh compression , also known as high‐pass mesh coding , is a popular technique for efficiently storing both static and dynamic triangle meshes that gained further recognition with the advent of perceptual mesh distortion evaluation metrics . Currently , the usual rule of thumb that drives the decision for a mesh compression algorithm is whether or not accuracy in absolute scale is required : Laplacian mesh encoding is chosen when perceptual quality is the main objective , while other techniques provide better results in terms of mechanistic error measures such as mean squared error .
Vertex coordinates in VRML scenes are represented by 6 decimal digits or even less . Mesh compression schemes may quantize vertex coordinates to even below precision of 4 decimal digits . The second contribution of this paper is a general technique for reducing processing time of watermark ( label ) extraction satisfying impatient users and enhancing robustness with respect to affine transformations and , in particular , vertex randomization attacks .
This has resulted in image compression being an important issue in reducing the cost of data storage and transmission time . Mesh based compression is one among them . Wavelet transform is a powerful mathematical tool with many unique qualities that are useful for image compression and processing applications .
The representation of 3D video is often based on dynamic 3D mesh model , which is reconstructed from multi-view video , plus surrounding texture information for rendering , so that arbitrary novel views can be synthesized accordingly . However , the dynamic 3D mesh model herein is not time-consistent , resulting in a difficulty in applying traditional mesh compression tools efficiently ( e.g. , MPEG-4 AFX 3DMC ) . In this paper , we modify the 3DMC algorithm for the coding and transmission of 3D video , taking its advantage of high coding efficiency for edge topologies and enhancing it with 3D motion estimation of vertices between two time-successive mesh models .
This paper describes CoolPression , a hybrid hardware-based significance compression technique for reducing energy in caches . The scheme exploits data compression opportunities at bit granularity by employing two compression schemes : a significance compression scheme that counts leading ones and zeros , and a dynamic zero compression technique . Based on actual data , the more energy-efficient scheme is selected dynamically to minimize bitline drives needed for each cache access .
As Recurrent Neural Networks is good at processing and making predictions based on series of data , we propose a Long-Short Term Memory network based model to select compression algorithm for input data blocks adaptively . Given a typical database benchmark , we implemented our model to formulate compression strategies for each data block and managed to reduce at most 15 % storage size than using a single compression algorithm scheme .
However , high compression rates come with visual artifacts that degrade the image quality and thus user experience . Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems .
Effective retargeting for image coding JPEG backward compatible format for 3D content representation Introduction to video compression Design and implementation of Motion-JPEG on Medical imaging Application Rate Control and Bit Allocation for JPEG Transcoding JPEG Image Encryption With Improved Format Compatibility and File Size Preservation MPEG-7 multimedia-based query format Modified DWT Based Medical Image Transmission Using Reduced Storage Space A new JPEG-based steganographic algorithm for mobile devices Electronic art history: an approach that works, but not without its glitches Lossless compression for x-ray mammogram images Evading android anti-malware by hiding malicious application inside images Image Reconstruction with Predictive Filter Flow Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Towards Conceptual Compression Deep Multi-Center Learning for Face Alignment Model-based coding of multiviewpoint imagery Making 3D textures practical A Hash based Approach for Secure Keyless Steganography in Lossless RGB Images Využití interaktivních a multimediálních prostředků ve výchovně-vzdělávacím procesu ve Zlínském kraji. A Wavelet Based Method for Detecting Multiple Encoding Rhythms in Neural Networks Rate-adapt'ive turbo-s sndromne schemne DeepMatching: Hierarchical Deformable Dense Matching Enhanced interframe coding based on morphological segmentation Energy efficient gas emission monitoring systems using Wireless Sensor Networks Development of AAC-codec for streaming in wireless mobile applications Comparison between unitary and causal approaches to backward adaptive transform coding of vectorial signals Towards the Synergy between Compression and Content-Based Analysis: A Pattern-Driven Approach Sinusoidal substitution — An integrated parametric tool for enhancement of transform-based perceptual audio coders A Parallel H.264/AVC Encoder Architecture Based on FPGA Combining wavelet and fractal coding for 3-D video coding Tensor completion for on-board compression of hyperspectral images Content Adaptive Optimization for Neural Image Compression Transform coding of images using trellis coded quantization Transform coding of monochrome and color images using trellis coded quantization High performance VLSI architecture for the trellis coded quantization Spatial Transform Coding of Color Images Progressive imaging on the Internet via WWW browser using adaptive block truncation coding Evaluation of DWT and DCT for irregular mesh-based motion compensation in predictive video coding Computation reduction in high-efficiency video coding based on the similarity of transform unit blocks Optimizing the transform complexity-quality tradeoff for hardware-accelerated HEVC video coding A wavelet-based progressive ECG compression using modified SPIHT Incrementally maintaining run-length encoded attributes in column stores Image encryption utilizing lossy image compression Bit Transposed Files Modified run length encoding scheme with introduction of bit stuffing for efficient data compression COMPARISON OF TWO ASCII ART EXTRACTION METHODS: A RUN-LENGTH ENCODING BASED METHOD AND A BYTE PATTERN BASED METHOD A run-length based algorithm for feature extraction from multi-target image Improving on the Algorithm of Raster Detection Plus Vector Calculation to Accelerate Process of Vector Intersection Adaptive Difference Compensation Vector Quantization Using Dynamic Image Block Adjustment Packet-size-Controlled ECG Compression Algorithm based on Discrete Wavelet Transform and Running Length Encoding Implementation and Application of Intersection Operation Based on Run-Length Encoding Implementation and Application of Intersection Operation Based on Run-Length Encoding Data Needles in the Haystack - Tackling Bit Flips in Lightweight Compressed Data Test compression for scan circuits using scan polarity adjustment and pinpoint test relaxation Data compression device, data compression method, and computer readable recording medium Application and optimization for Lattice Boltzmann Method in calculation of fluid permeability Compressing large polygonal models Error propagation control in Laplacian mesh compression Affine Invariant Watermarks for 3D Polygonal and NURBS Based Models Performance evaluation of 3D MR Image Compression Using Wavelet Coding Coding of Dynamic 3D Mesh Model for 3D Video Transmission CoolPression - a hybrid significance compression technique for reducing energy in caches Adaptive Compression Algorithm Selection Using LSTM Network in Column-oriented Database Image Reconstruction with Predictive Filter Flow
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ISCAS WIAMIS SIGUCCS Image Processing: Algorithms and Systems ArXiv ArXiv NIPS Neurocomputing VCIP Pacific Conference on Computer Graphics and Applications IWANN (1) International Journal of Computer Vision ICASSP DCC ICASSP ICIP (1) ICIP ICASSP ICIP (2) VCIP DASIP IDEAS VLDB ICITST BIOSIGNALS CSSE (4) DATA (Revised Selected Papers) ASP-DAC IEEE Visualization ISW PSIVT (1) SoCC ArXiv
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