Reduction In File Size Research Articles

Study and Implementation of Image Compression using Different Wavelets

Transmission applications are utilized in broadcast,remote detecting by means of satellite, flying machine, radar, video chatting, PC interchanges, copy transmission, and so on. The cost of storage and transmission of images across a network is important now-a-days, as a huge collection of images in the form of databases are available on the Net. The reduction in file size is necessary to meet the bandwidth requirements for many transmission systems, and to reduce storage costs.

Image Steganography In Securing Sound File Using Arithmetic Coding Algorithm, Triple Data Encryption Standard (3DES) and Modified Least Significant Bit (MLSB)

The amount of data inserted in the form of audio samples that use 8 bits with LSB algorithm, affect the value of PSNR which resulted in changes in image quality of the insertion (fidelity). So in this research will be inserted audio samples using 5 bits with MLSB algorithm to reduce the number of data insertion where previously the audio sample will be compressed with Arithmetic Coding algorithm to reduce file size. In this research will also be encryption using Triple DES algorithm to better secure audio samples. The result of this research is the value of PSNR more than 50dB so it can be concluded that the image quality is still good because the value of PSNR has exceeded 40dB.

Video Adaptation Algorithm for Mobile Learning Environment

This paper focuses on developing a static video adaptation algorithm in order to present suitable video content according to device’s capabilities, bandwidth availability and user preferences. Server-based content adaptation approach has been implemented to cater accessibility problem of the current mobile learning system. The video content is adaptively generated based on recognize mobile phone capabilities and sent back to the requested client. In the proposed algorithm, the decision mechanism in the video adaptation algorithm is based on three parameters, which are the device characteristics, network bandwidth and user preferences. This work extends the accessibility of the mobile learning system for access via mobile wireless technology specifically for the access of the video content. The result shows that the proposed algorithm offers very significant performance improvement which is a reduction of network access latency and file size. The proposed algorithm also provides low complexity and can be implemented efficiently.

Evaluation of a confocal WSI scanner for FISH slide imaging and image analysis

Background: Technological advances contribute to the maturation of digital pathology in clinical and research applications. However, there are only few reports on fluorescence scanning especially on confocal fluorescence imaging technology in digital pathology, which has superior depth resolution compared to wide-field fluorescence imaging. Here, we explored the features of a confocal WSI scanner for typical diagnostic and research imaging applications of fluorescence in situ hybridization (FISH) assay.Methods: Multi-layer stacking (Z-stack) which stores all image information from each layer, and extended focus which stores the optimal image information from all scanned layers, featured in the Pannoramic Confocal scanner (3DHISTECH Ltd., Budapest, Hungary) were employed in digitizing 14 FISH slides (ALK, EGFR, and multi-gene). The slides were scanned with a 40× water immersion objective producing a final image with pixel resolution of 0.1625 µm/pixel. Z-stack and extended focus were used for N=6, 13, and 26 multiple layers scanning at 1, 0.4, and 0.2 µm depth intervals respectively. Single-layer scanning was also done for comparison. Scanning time and resultant file size were recorded. The CaseViewer from 3DHISTECH was used to visualize images and export the annotated regions, and the exported images were further analyzed in Imaris (Bitplane, Zurich, Switzerland) for 3-dimensional reconstruction, nuclear segmentation, and the quantification and co-localization analysis of dots inside nuclei. Quantification data from Imaris were imported into Excel for statistic analysis.Results: Confocal fluorescence scanning of FISH slides enabled sharper image than wide-field scanning, although it required longer scanning time and larger file storage. More intra-nuclear dots were quantified from multi-layer Z-stack images than single-layer images, and the Z-stack increased scanning time and image file size. Furthermore, there were a reduced in the number of dots and an increased in the number of co-localized dots in extended-focus images compared to Z-stack. Dots in multiple channels were quantified and analyzed automatically, which supports clinical diagnosis of gene amplification, deletion, and translocation. Three-dimensional reconstruction of Z-stack produced precise measurement of spatial distance, which supports molecular research.Conclusion: Confocal provides sharper image than wide-field for FISH slide scanning. Extended focus reduces file size and storage, but could cause inaccurate analysis due to misinterpretation of overlapping information. Z-stack scanning provides high volume image information for spatial analysis. We foresee confocal multi-layer scanning as a digital pathology application tool for FISH imaging in both clinical and research in future.

Lossless Compression of VC Shares in RGB Color Space

Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human vision if the correct key image is used. In Visual Cryptography the reconstructed image after decryption process encounters a major problem of Pixel expansion. This is overcome in this proposed method by minimizing the memory size using lossless image compression techniques. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. Hybrid techniques are used in this proposed method as it can exploit multiple kinds of redundant information Keywords: Visual Cryptography; HVS; Image Compression; Vector Quantization; Run Length Encoding; Huffman Coding

Multiple hypothesis colorization and its application to image compression

In this work we focus on the problem of colorization for image compression. Since color information occupies a large proportion of the total storage size of an image, a method that can predict accurate color from its grayscale version can produce a dramatic reduction in image file size. But colorization for compression poses several challenges. First, while colorization for artistic purposes simply involves predicting plausible chroma, colorization for compression requires generating output colors that are as close as possible to the ground truth. Second, many objects in the real world exhibit multiple possible colors. Thus, in order to disambiguate the colorization problem some additional information must be stored to reproduce the true colors with good accuracy. To account for the multimodal color distribution of objects we propose a deep tree-structured network that generates for every pixel multiple color hypotheses, as opposed to a single color produced by most prior colorization approaches. We show how to leverage the multimodal output of our model to reproduce with high fidelity the true colors of an image by storing very little additional information. In the experiments we show that our proposed method outperforms traditional JPEG color coding by a large margin, producing colors that are nearly indistinguishable from the ground truth at the storage cost of just a few hundred bytes for high-resolution pictures!

Applying n-bit floating point numbers and integers, and the n-bit filter of HDF5 to reduce file sizes of remote sensing products in memory-sensitive environments

Memory sensitive applications for remote sensing data require memory-optimized data types in remote sensing products. Hierarchical Data Format version 5 (HDF5) offers user defined floating point numbers and integers and the n-bit filter to create data types optimized for memory consumption. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) applies a compaction scheme to the disseminated products of the Day and Night Band (DNB) data of Suomi National Polar-orbiting Partnership (S-NPP) satellite’s instrument Visible Infrared Imager Radiometer Suite (VIIRS) through the EUMETSAT Advanced Retransmission Service, converting the original 32 bits floating point numbers to user defined floating point numbers in combination with the n-bit filter for the radiance dataset of the product. The radiance dataset requires a floating point representation due to the high dynamic range of the DNB. A compression factor of 1.96 is reached by using an automatically determined exponent size and an 8 bits trailing significand and thus reducing the bandwidth requirements for dissemination. It is shown how the parameters needed for user defined floating point numbers are derived or determined automatically based on the data present in a product.

A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree

Biomedical signals enfold much crucial clinical information. Cardiac imperfection includes information on the morphology of its electrical signals. These signals are classically recorded over a considerable period, so the size of data file becomes bulky and hence compression is essential. This paper focuses on the implementation of electrocardiogram signal compression using wavelet-based progressive coding such as set partitioning in hierarchical tree and its modified version to achieve improvement in the speed at low bit rate. We obtained compression ratio up to 22:1 for MIT-BIH arrhythmia database record number 117 with a percent mean square difference of 0.9 and 0.73 % using orthogonal and biorthogonal wavelets, respectively. The coders accomplish bit rate control and produce a bit stream that is progressive in quality. It facilitates the user to trim the bit stream at desired point and make required quality restoration for the reduced file size with user-defined compression ratio or bit rate.

Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic Resonance (MRI) Images

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes.

Vector Quantization for Satellite Image Compression

Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows as to store more data in less memory and speed up the transmission process in low bandwidth also, in case of satellite images it reduces the time required for the image to reach the ground station. In order to increase the transmission process compression plays an important role in remote sensing images. This paper presents a coding scheme for satellite images using Vector Quantization. And it is a well-known technique for signal compression, and it is also the generalization of the scalar quantization. The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and decompressed image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

Quantized DCT Coefficient Category Address Encryption for JPEG Image

Digital image encryption is widely used for image data security. JPEG standard compresses image with great performance on reducing file size. Thus, to encrypt an image in JPEG format we should keep the quality of original image and reduced size. This paper proposes a JPEG image encryption scheme based on quantized DC and non-zero AC coefficients inner category scrambling. Instead of coefficient value encryption, the address of coefficient is encrypted to get the address of cipher text. Then 8*8 blocks are shuffled. Chaotic iteration is employed to generate chaotic sequences for address scrambling and block shuffling. Analysis of simulation shows the proposed scheme is resistant to common attacks. Moreover, the proposed method keeps the file size of the encrypted image in an acceptable range compared with the plain text. To enlarge the cipher text possible space and improve the resistance to sophisticated attacks, several additional procedures are further developed. Contrast experiments verify these procedures can refine the proposed scheme and achieve significant improvements.

A wavelet-based progressive ECG compression using modified SPIHT

An Electrocardiogram (ECG) signal contains much vital clinical information. These signals include information on cardiac imperfection. These signals recorded over an extended period result in large file size. Thus, compression is essential. 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. The coders achieve bit rate control and produce a bitstream that is progressive in quality. The user can trim the bitstream at any point and make the best quality restoration for reduced file size using required values of compression ratio.

Consolidating CCDs from multiple data sources: a modular approach.

Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient's various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data. The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system's performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed. All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49% in Problems, 60.6% in Medications, and 79% in Allergies) and reduced the size of the documents (57.5%) as well as the number of lines in each document (58%). The system executed at a rate of approximately 0.009-0.03 s per rule depending on the complexity of the rule. Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.

Покращення характеристик алгоритму стиснення jpeg з метою підвищення швидкості завантаження сайтів

The possibility of increasing the speed of download sites by decreasing the size of the images loaded, ie, their compression. To ensure a smaller file size and save it as modified alhoritm compression JPEG. The modification is to replace the discrete cosine transform wavelet transformation. In the conduct of experimental study used 50% JPEG compression and modified compression 50% JPEG. The use of the modified compression method can reduce file size by 25% while maintaining file quality. This makes it possible to speed up downloading of site.

SELF-EMBEDDING VIDEO WATERMARKING USING DUAL ORTHOGONAL COMPLEX CONTOURLET TRANSFORM WITH AUTOCORRELATION SYSTEM

This paper presents a novel non-blind watermarking algorithm using dual orthogonal complex contourlet transform. The dual orthogonal complex contourlet transform is preferred for watermarking because of its ability to capture the directional edges and contours superior to other transforms such as cosine transform, wavelet transform, etc. Digital image and video in their raw form require an enormous amount of storage capacity and the huge data systems also contain a lot of redundant information.Compression also increases the capacity of the communication channel. Image Compression using SPIHT Set Partitioning in Hierarchical Trees algorithm based on Huffman coding technique. SPIHT algorithm is the lossless compression algorithms reduce file size with no loss in image quality and comparing the final results in terms of bit error rate, PSNR and MSE.

Adaptation of an internet‐based DICOM viewer for use with the Medical Imaging Resource Center – Teaching File System (MIRC‐TFS)

DICOM is the standard file format for medical imaging; however, storage, transmission and viewing of these images over the internet for teaching and learning purposes outside of the clinical setting presents a challenge as they do not lend themselves well to rapid display and interrogation due to their atypical file format. The aim of this project was to utilize the web technology standard HTML 5 offers to deploy a DICOM viewer that functions well, directly in the user's browser, without the need for complex client side‐applications, application switching, or user login requirements. To achieve this we have adapted the open source HTML 5 DICOM viewer, Oviyam, for integration with the Radiological Society of North America's Medical Imaging Resource Center – Teaching File System (MIRC‐TFS) on a virtual server to facilitate access to DICOM objects for teaching and learning. For learners a plugin was developed to transform the DICOM images in MIRC‐TFS into JPEG format resulting in a reduction in file size allowing users to view the images more rapidly while supporting higher user traffic for viewing images concurrently, as frequently occurs with large class sizes. In addition, a second plugin was developed for BlackBoard Learn that allows instructors to browse the MIRC‐TFS teaching cases and insert them alongside their other course materials. The tool set and image viewing capabilities of Oviyam were enhanced to include annotation tools allowing students to create marked up versions of the images for evaluation of their knowledge. The deployment of this solution has significantly facilitated the interaction of teachers and learners with DICOM based images for use in their academic environment.

Novel Method of Image Compression using Angular Domain Concept to Achieve High Compression Rate

Image compression plays vital role in the field of the corporate world, entertainment industry, multimedia, education communications and even at home. The main objective of Image compression is to minimize the size of image in bytes by reducing the redundancy of the image data without degrading the quality of image that results in the reduction of file size so that more images can be stored in a given amount of disk or memory space and also reduces the time required to send the images over the network. Inspired by the recent advancements in the image compression techniques, we proposed an image compression framework based on the pixel-wise fidelity. The proposed method is used to reduce the bit rate of the pixels of the image for the compression by using angular transformation. In the proposed algorithm we used the normalization technique and the inverse sine transformation to get the optimized and compressed image as an output. Finally the results are compared with different methods for MSE, PSNR and the visual appearance after decoding of image.

Precision spectral peak frequency measurement using a window leakage ratio function

For power spectra of signals consisting of stationary sinusoids mixed with random noise, the frequency and amplitude of a spectral peak can be estimated with greater accuracy than the nearest frequency bin of the Fourier transform by exploiting the spectral leakage characteristics for the particular data window used. Techniques such as linear interpolation or an amplitude weighted average have inadequate precision due to the nonlinear leakage into adjacent bins and the dependence on data window type. This paper offers a new general algorithm presented using the Fourier coefficients ck of the input data window to produce a function which is the ratio of the side-bin amplitudes of the window in the frequency domain. The ratio function allows one to use the amplitudes of the adjacent bins of a spectral peak to precisely estimate the peak frequency and amplitude when the frequency does not lie exactly on a frequency bin (in between the discrete bins of a Fourier transform). Examples are provided for a number of popular data windows. The ratio function can be most easily implemented using a simplified log-ratio function for the window side bin magnitudes. A statistical analysis provides a useful frequency estimation error estimate given the signal-to-noise ratio of the spectral peak based on an approximation of the ratio of non-zero mean Gaussian variables. The benefits of this technique are not just improved estimation accuracy for amplitude and frequency, but also allow large spectral data files to be accurately reduced in size for remote monitoring of vibration spectra. An example is given of a methodology for reduction of spectral data file size without the loss of important signals for analysis where the file size is reduced by 88% with only a few percent error, which is mostly confined to the background noise in the reconstructed spectrum.

Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization

Reduces the file size and increase the performances of classification and intrusion detection technique used in current research trend. The reduction of file size and number of attribute used dimension reduction algorithm and optimization algorithm. Various authors used genetic algorithm, ANT colony optimization and neural network. In this paper used particle of swarm optimization technique for feature reduction and feature selection for support vector machine classification process. The proposed algorithm implemented in MATLAB software and used DARPA dataset for evaluation of proposed method. Our empirical result shows that better detection ratio in compression of other exiting technique such as FCMNN, GSVM. Keyword IDS, Feature Reduction, SVM, PSO

New Algorithm to Reduce File Size and Bandwidth of Hashed Digital Signature for Image Steganography Using LSB

In computer cryptography, digital signature is one of the best cryptography systems. Public-private keys are used to pass sensitive information however it can also be used to provide authentication on the particulars of a sender. Proposed algorithm is a new design and simple implementation mechanism for producing a digital signature. Some applications such as multi agent systems transferred data with small size and capacity. The new scheme minimizes the size of original file and gives us a dynamic and smaller hashed message output. In this algorithm load the original files then hashed the message and encode it with the private key of sender. Finally modify the established code into a unique digital signature at Base 16. We concentrate on designing and implementation of functions of algorithm.