Showing papers on "Image file formats published in 2015"

Exploiting similarities between secret and cover images for improved embedding efficiency and security in digital steganography

Abstract: The rapid advancements in digital communication technology and huge increase in computer power have generated an exponential growth in the use of the Internet for various commercial, governmental and social interactions that involve transmission of a variety of complex data and multimedia objects. Securing the content of sensitive as well as personal transactions over open networks while ensuring the privacy of information has become essential but increasingly challenging. Therefore, information and multimedia security research area attracts more and more interest, and its scope of applications expands significantly. Communication security mechanisms have been investigated and developed to protect information privacy with Encryption and Steganography providing the two most obvious solutions. Encrypting a secret message transforms it to a noise-like data which is observable but meaningless, while Steganography conceals the very existence of secret information by hiding in mundane communication that does not attract unwelcome snooping. Digital steganography is concerned with using images, videos and audio signals as cover objects for hiding secret bit-streams. Suitability of media files for such purposes is due to the high degree of redundancy as well as being the most widely exchanged digital data. Over the last two decades, there has been a plethora of research that aim to develop new hiding schemes to overcome the variety of challenges relating to imperceptibility of the hidden secrets, payload capacity, efficiency of embedding and robustness against steganalysis attacks. Most existing techniques treat secrets as random bit-streams even when dealing with non-random signals such as images that may add to the toughness of the challenges.This thesis is devoted to investigate and develop steganography schemes for embedding secret images in image files. While many existing schemes have been developed to perform well with respect to one or more of the above objectives, we aim to achieve optimal performance in terms of all these objectives. We shall only be concerned with embedding secret images in the spatial domain of cover images. The main difficulty in addressing the different challenges stems from the fact that the act of embedding results in changing cover image pixel values that cannot be avoided, although these changes may not be easy to detect by the human eye. These pixel changes is a consequence of dissimilarity between the cover LSB plane and the secretimage bit-stream, and result in changes to the statistical parameters of stego-image bit-planes as well as to local image features. Steganalysis tools exploit these effects to model targeted as well as blind attacks. These challenges are usually dealt with by randomising the changes to the LSB, using different/multiple bit-planes to embed one or more secret bits using elaborate schemes, or embedding in certain regions that are noise-tolerant. Our innovative approach to deal with these challenges is first to develop some image procedures and models that result in increasing similarity between the cover image LSB plane and the secret image bit-stream. This will be achieved in two novel steps involving manipulation of both the secret image and the cover image, prior to embedding, that result a higher 0:1 ratio in both the secret bit-stream and the cover pixels‘ LSB plane. For the secret images, we exploit the fact that image pixel values are in general neither uniformly distributed, as is the case of random secrets, nor spatially stationary. We shall develop three secret image pre-processing algorithms to transform the secret image bit-stream for increased 0:1 ratio. Two of these are similar, but one in the spatial domain and the other in the Wavelet domain. In both cases, the most frequent pixels are mapped onto bytes with more 0s. The third method, process blocks by subtracting their means from their pixel values and hence reducing the require number of bits to represent these blocks. In other words, this third algorithm also reduces the length of the secret image bit-stream without loss of information. We shall demonstrate that these algorithms yield a significant increase in the secret image bit-stream 0:1 ratio, the one that based on the Wavelet domain is the best-performing with 80% ratio.For the cover images, we exploit the fact that pixel value decomposition schemes, based on Fibonacci or other defining sequences that differ from the usual binary scheme, expand the number of bit-planes and thereby may help increase the 0:1 ratio in cover image LSB plane. We investigate some such existing techniques and demonstrate that these schemes indeed lead to increased 0:1 ratio in the corresponding cover image LSB plane. We also develop a new extension of the binary decomposition scheme that is the best-performing one with 77% ratio. We exploit the above two steps strategy to propose a bit-plane(s) mapping embedding technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This is motivated by the observation that non-binary pixel decomposition schemes also result in decreasing the number of possible patterns for the three first bit-planes to 4 or 5 instead of 8. We shall demonstrate that the combination of the mapping-based embedding scheme and the two steps strategy produces stego-images that have minimal distortion, i.e. reducing the number of the cover pixels changes after message embedding and increasing embedding efficiency. We shall also demonstrate that these schemes result in reasonable stego-image quality and are robust against all the targeted steganalysis tools but not against the blind SRM tool. We shall finally identify possible future work to achieve robustness against SRM at some payload rates and further improve stego-image quality.

A mobile app for securely capturing and transferring clinical images to the electronic health record: description and preliminary usability study.

Abstract: Background: Photographs are important tools to record, track, and communicate clinical findings. Mobile devices with high-resolution cameras are now ubiquitous, giving clinicians the opportunity to capture and share images from the bedside. However, secure and efficient ways to manage and share digital images are lacking. Objective: The aim of this study is to describe the implementation of a secure application for capturing and storing clinical images in the electronic health record (EHR), and to describe initial user experiences. Methods: We developed CliniCam, a secure Apple iOS (iPhone, iPad) application that allows for user authentication, patient selection, image capture, image annotation, and storage of images as a Portable Document Format (PDF) file in the EHR. We leveraged our organization’s enterprise service-oriented architecture to transmit the image file from CliniCam to our enterprise clinical data repository. There is no permanent storage of protected health information on the mobile device. CliniCam also required connection to our organization’s secure WiFi network. Resident physicians from emergency medicine, internal medicine, and dermatology used CliniCam in clinical practice for one month. They were then asked to complete a survey on their experience. We analyzed the survey results using descriptive statistics. Results: Twenty-eight physicians participated and 19/28 (68%) completed the survey. Of the respondents who used CliniCam, 89% found it useful or very useful for clinical practice and easy to use, and wanted to continue using the app. Respondents provided constructive feedback on location of the photos in the EHR, preferring to have photos embedded in (or linked to) clinical notes instead of storing them as separate PDFs within the EHR. Some users experienced difficulty with WiFi connectivity which was addressed by enhancing CliniCam to check for connectivity on launch. Conclusions: CliniCam was implemented successfully and found to be easy to use and useful for clinical practice. CliniCam is now available to all clinical users in our hospital, providing a secure and efficient way to capture clinical images and to insert them into the EHR. Future clinical image apps should more closely link clinical images and clinical documentation and consider enabling secure transmission over public WiFi or cellular networks. [JMIR Mhealth Uhealth 2015;3(1):e1]

Framework for real-time brokering of digital content delivery

Abstract: A method of transmitting digital content via a communication network. The method comprises receiving by a computer a request for a uniform resource identifier (URI), determining by a computer based on the request for the uniform resource identifier a communication service provider associated with a communication device, and when the communication service provider is affiliated with a digital content custom delivery offer building system, transmitting by a computer an image file to be presented by the communication device.

Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation

Abstract: Background/Objectives: Image segmentation is one of the fundamental techniques in image processing. During past few years, the image processing mechanisms are extensively used in various medical fields for early stage detection, separation and identification of diseases; in this, the time consumption is an important criteria to discover the diseases for the patient. Methods/Statistical Analysis: This research work analyses about the detection and separation of brain tumor through Magnetic Resonance Imaging (MRI) medical images using Particle Swarm Optimization (PSO), a heuristic global optimization method based on swarm intelligence. The algorithm is widely used and rapidly developed for its ease implementation. This work has four stages that includes conversion, implementation, selection and extraction. Findings: The research work starts with converting the Digital Imaging and Communications in Medicine (DICOM) into image file format which is the first stage. Applying the PSO algorithm with the change in the values of n (segmentation level) is the second stage. Based on the time, selecting the best resultant images is the third stage. The final stage is extraction of tumor affected region with the suitable filtering techniques. The research work takes the axial and coronal plane of the Magnetic Resonance (MRI) images. Finally, this work concludes with the extraction of the resultant image, which is taken as input, and using the best filtering technique the affected region is easily separated and identified efficiently. The work also identifies the best suitable plane for the PSO algorithm. Applications/Improvements: The same PSO algorithm is applied to find the size and the type of calcifications in MRI brain images and are extracted in future. The possibilities of using other algorithms are also considered for further implementation.

Image Formats and Related Methods and Apparatuses

Abstract: Image data may be color graded, distributed and viewed on target displays. Mappings that preserve mid-range points and mid-range contrast may be applied to view the image data for color grading and to prepare the image data for display on a target display. The image data may be expanded to exploit the dynamic range of the target display without affecting mid-tone values.

Carving Orphaned JPEG File Fragments

Abstract: File carving techniques allow for recovery of files from storage devices in the absence of any file system metadata. When data are encoded and compressed, the current paradigm of carving requires the knowledge of the compression and encoding settings to succeed. In this paper, we advance the state of the art in JPEG file carving by introducing the ability to recover fragments of a JPEG file when the associated file header is missing. To realize this, we examined JPEG file headers of a large number of images collected from Flickr photo sharing site to identify their structural characteristics. Our carving approach utilizes this information in a new technique that performs two tasks. First, it decompresses the incomplete file data to obtain a spatial domain representation. Second, it determines the spatial domain parameters to produce a perceptually meaningful image. Recovery results on a variety of JPEG file fragments show that given the knowledge of Huffman code tables, our technique can very reliably identify the remaining decoder settings for all fragments of size 4 KiB or above. Although errors due to detection of image width, placement of image blocks, and color and brightness adjustments can occur, these errors reduce significantly when fragment sizes are >32 KiB.

Cloud system and method of displaying, by cloud system, content

Abstract: Provided is a cloud providing a cloud service for displaying content, the cloud including: a first server configured to receive a sharing request for content, from an electronic device; a second server configured to determine whether the content is displayable on a display device connected to the cloud, based on a type of a file format of the content; a third server configured to render the content and generate an image file from the rendered content, when it is determined that the content is not displayable; and a fourth server configured to control display of one of the content and the image file on the display device.

Ultra-high compression of images based on deep learning

Abstract: A system for machine learning model parameters for image compression, including partitioning image files into a first set of regions, determining a first set of machine learned model parameters based on the regions, the first set of machine learned model parameters representing a first level of patterns in the image files, constructing a representation of each of the regions based on the first set of machine learned model parameters, constructing representations of the image files by combining the representations of the regions in the first set of regions, partitioning the representations of the image files into a second set of regions, and determining a second set of machine learned model parameters based on the second set of regions, the second set of machine learned model parameters representing a second level of patterns in the image files.

Intra-operative medical image viewing system and method

Abstract: An intra-operative medical image viewing system can allow a surgeon to maintain a viewing perspective on the patient while calling-up visual images on-the-fly. A digital image source has at least one image file representative of an anatomical or pathological feature of a patient. A display is worn by the surgeon or positioned between the surgeon and her patient during surgery. The display is selectively transparent, and exhibits to the surgeon an image derived from the image file. An image control unit retrieves the image file from the image source and controls the display so that at least a portion of the image depiction can be exhibited and modified at will by the surgeon. A plurality of peripheral devices are each configured to receive an image control input from the surgeon and, in response, generate an image control signal. Each peripheral accepts a different user-interface modality.

Method and device for embedded sim provisioning

Abstract: A method and an electronic device are provided for embedded SIM (eSIM) provisioning. The electronic device includes a first interface configured to transmit a request message for requesting a profile from a profile generation server, and to receive a plurality of packets associated with installation of the profile, in response to the request message; a processor configured to generate an image file from the plurality of packets; an authentication module configured to perform authentication with the profile generation server and authenticate an electronic device including an embedded SIM (eSIM); and a second interface configured to connect the server device to the electronic device, and to transmit the image file to the electronic device, if the electronic device is authenticated.

Image based measurement system

Abstract: A Quantified Image Measurement System that creates accurate physical measurement data from digital pictures is disclosed. The system can use any image format and enhances the image file with measurement data and data transformation information that enables the creation of any type of geometrical or dimensional measurement from the stored photograph. This file containing the original digital image along with the supplemental data is referred to as a Quantified Image File or QIF. The QIF can be shared with other systems via email, cloud syncing or other types of sharing technology. Once shared, existing systems such as CAD applications or web/cloud servers can use the QIF and the associated QIF processing software routines to extract physical measurement data and use the data for subsequent processing or building geometrically accurate models of the objects or scene in the image. Additionally smart phones and other portable devices can use the QIF to make measurements on the spot or share between portable devices. In addition, the quantified image measurement system of this invention eliminates the need for capturing the image from any particular viewpoint by using multiple reference points and software algorithms to correct for any off-angle distortions.

Slide Set: Reproducible image analysis and batch processing with ImageJ.

Abstract: Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets common in biology. Here we present the Slide Set plugin for ImageJ, which provides a framework for reproducible image analysis and batch processing. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution.

Vector-based face recognition algorithm and image search system

Abstract: Systems and methods for performing face recognition and image searching are provided. A system for face recognition and image searching includes an ingestion system, a search system, a user device, and a database of galley files that include feature vectors. The ingestion system crawls the internet starting with a seed URL to scrape image files and generate feature vectors. Feature vectors of images input by a user may be compared by the search system to feature vectors in the gallery files. A method for generating feature vectors includes landmark detection, component aligning, texture mapping, vector computation, comparing cluster centers defined by vectors stored in a database with vectors generated based on an input image, linear discriminant analysis, and principal component analysis.

Fast LZW Compression Using a GPU

Abstract: The LZW compression is a well known patented lossless compression method used in Unix file compression utility "compress" and in GIF and TIFF image formats. It converts an input string of characters (or 8-bit unsigned integers) into a string of codes using a code table (or dictionary) that maps strings into codes. Since the code table is generated by repeatedly adding newly appeared substrings during the conversion, it is very hard to parallelize LZW compression. The main purpose of this paper is to accelerate LZW compression for TIFF images using a CUDA-enabled GPU. Our goal is to implement LZW compression algorithm using several acceleration techniques using CUDA, although it is a very hard task. Suppose that a GPU generates a resulting image generated by a computer graphics or image processing CUDA program and we want to archive it as a LZW-compressed TIFF image in the SSD connected to the host PC. We focused on the following two scenarios. Scenario~1: the resulting image is compressed using a GPU and written in the SSD through the host PC, and Scenario~2: it is transferred to the host PC, and compressed and written in the SSD using a CPU. The experimental results using NVIDIA GeForce GTX 980 and Intel Core i7 4790 show that Scenario 1 using our LZW compression implemented in a GPU is about 3 times faster than Scenario 2. From this fact, we can say that it makes sense to compress images using a GPU to archive them in the SSD.

Systems and methods for authenticating photographic image data

Abstract: The present disclosure provides systems and methods for authenticating photographic data. In one embodiment, a method comprises providing an image authentication application for use on a client device, the application configured to control image capture and transmission; receiving an image data file from the application at the authentication server comprising a photographic image captured by the application and metadata associated therewith; applying a watermark to the photographic image to create a watermarked image; applying date and time information to the tagged image; applying location information to the tagged image; creating a web address associated with the image data file; uploading the photographic image, the tagged image, or both to the web address; and transmitting an authenticated image file to the client device, the authenticated image file comprising one or more of: the watermarked image, the photographic image, the date and time information, geographic information, and the web address.

Information processing apparatus, method of controlling the same, and storage medium

Abstract: There is provided with an information processing apparatus and a method of controlling the same The apparatus sets a naming rule for an image file and determines whether or not the set naming rule satisfies a predetermined condition If it is determined that the naming rule does not satisfy the predetermined condition, the information processing apparatus warns a user On the other hand, if it is determined that the naming rule satisfies the predetermined condition, the information processing apparatus generates a file name of the image file in accordance with the set naming rule, and stores the image file with the file name

Framework for data extraction by examples

Abstract: Various technologies described herein pertain to controlling automated programming for extracting data from an input document. Examples indicative of the data to extract from the input document can be received. The examples can include highlighted regions on the input document. Moreover, the input document can be a semi-structured document (e.g. a text file, a log file, a word processor document, a semi-structured spreadsheet, a webpage, a fixed-layout document, an image file, etc.). Further, an extraction program for extracting the data from the input document can be synthesized based on the examples. The extraction program can be synthesized in a domain specific language (DSL) for a type of the input document. Moreover, the extraction program can be executed on the input document to extract an instance of an output data schema.

An efficient image aesthetic analysis system using Hadoop

Abstract: Assessing aesthetic appeal of images is a highly subjective task and has attracted a lot of research interests recently. Prior researchers have developed several aesthetic analysis systems on standalone computers. However, it is challenging to run the algorithms on mobile devices since the process of aesthetic analysis is quite complicated and time-consuming, especially for large amount of images. Hadoop is a popular technology for big data processing on cloud to offload computing burden from terminals. However it has NOT been used on image aesthetic yet. In this paper, we present an image aesthetic analysis system based on Hadoop framework to provide an efficiency solution and better user experience. We address several major problems: (1) adapt MapReduce for image data format and aesthetic analysis algorithms; (2) improve computing performance for large amount of small image files; (3) design a dynamic scheduling mechanism to optimize concurrent multiple users' requests; (4) design an effective commutation service between cloud and terminals. Experimental results demonstrate significant performance improvements with our system. At the same time, the system efficiency increases linearly with the expansion of the slaves in Hadoop. An efficient image aesthetic analysis system is designed based on Hadoop framework.It provides efficiency solution and good user experience for mobile device users.The Hadoop system is adapted for image data format and aesthetic analysis algorithms.The Hadoop system is optimized for processing large amount of small files efficiently.The scheduling mechanism of the system is optimized for multiple users' requests.An effective communication service between cloud server and terminals is designed.

MIPr - a framework for distributed image processing using Hadoop

Abstract: Nowadays, the sizes of image collections are increasing dramatically and reaching petabytes of data. Such large volumes cannot be analyzed on personal computer within a reasonable time. Therefore, processing of modern image collections requires distributed computing. This paper presents a MapReduce Image Processing framework (MIPr), which provides the ability to use distributed computing for image processing. MIPr is based on MapReduce and its open source implementation Apache Hadoop. MIPr provides various forms of image representations in Hadoop internal format and the input/output tools for integration of image processing into Hadoop data workflow. The image formats in the MIPr framework are based on the popular image processing libraries. Furthermore, the MIPr includes the high-level Image processing API for developers who are not familiar with Hadoop. This API allows to create sequential functions that process one image or a group of related images. The MIPr framework applies such functions to the large amount of images in parallel. In addition, MIPr includes MapReduce implementations of popular image processing algorithms, which can be used for distributed image processing without any software development. The MIPr framework significantly simplifies image processing in Hadoop distributed environment.

The High Efficiency Image File Format Standard [Standards in a Nutshell]

Abstract: The High Efficiency Image File Format (HEIF) is a standard developed by the Moving Picture Experts Group (MPEG) for the storage of images and image sequences. The standard facilitates file encapsulation of data coded according to the High Efficiency Video Coding (HEVC) standard. The compression performance of HEVC is superior to any alternative image or image sequence coding format. HEIF includes a rich set of features building on top of the widely used ISO Base Media File Format (ISOBMFF), making HEIF superior feature-wise compared to other image file formats. This article provides an overview of the performance, features, and design of HEIF.

Saving type duplicated data deleting method in cloud storage system

Abstract: The invention discloses a saving type duplicated data deleting method in a cloud storage system. The cloud storage system is composed of a client for operating files, a metadata server for storing metadata information of a file system, a secondary metadata server for synchronous backup of image files and operation logs of the metadata and storage nodes for storing data blocks. The method comprises five steps. In view of the dynamism of data in the cloud storage system and considering the characteristics of the data, the data are divided into hot data and non-hot data, different duplicated deleting times are applied to different data to guarantee better performance of the system, and the effect of shortening the system response time is better.

A Review: Image Interpolation Techniques forImage Scaling

Abstract: The growing interest in image scaling is mainly due to the availability of digital imaging devices such as, digital cameras, digital camcorders, 3G mobile handsets, high definition monitors etc. Scaling a digital image is a demanding and very important area of research. Image scaling is an important image processing operation applied in diverse areas in computer graphics. Image scaling can be especially useful when one needs to reduce image file size for email and web documents or increase image size for printing, GIS observation, medical diagnostic etc. With the recent advances in imaging technology, digital images have become an important component of media distribution. In addition, a variety of displays can be used for image viewing, ranging fromhigh-resolution computer monitors to TV screens and low-resolution mobile devices.This paper is focused on different image scaling techniqueswith intent that review to be useful to researchers and practitioners interested in image Scaling.

Approximate Computing in Chrominance Cache for Image/Video Processing

Abstract: An approximate computing method for chrominance data of the video/image sequences is proposed in this paper, which is implemented in one hybrid cache architecture. This approach fully exploits the fact that the sensitivity of chrominance is much lower than that of luminance to reduce off-chip memory access of chrominance data. For the 4:2:0 image format, the proposed approximate computing method can achieve 25% reduction in off-chip memory access with the 2×2 basic approximate unit. Moreover, for the image formats of 4:2:2 and 4:4:4, it has about 37.5% and 50% access reduction of off-chip memory, respectively, with acceptable degradation of image quality to human visual system.

An evaluation of Error Level Analysis in image forensics

Abstract: The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, the Error Level Analysis (ELA) technique was evaluated with different types of image tampering, including JPEG compression, image splicing, copy-move and image retouching. From the experiment, the ELA showed reliability with JPEG compression, image splicing and image retouching forgery.

Image file treating method, server and image display device

Abstract: The invention discloses an image file treating method, a server and an image display device. The method is applied to the server and comprises the steps of receiving an image file uploaded by an image acquiring device; acquiring a label of each image file; determining one or a plurality of category labels in match with the labels of the image files based on the created category label; building classification correspondence relationship between the image files and the matched one or the plurality of category labels; responding to the image display request from the image display device; determining the one or the plurality of image files and the category correspondence relationship to be displayed in the image display device; sending the determined one or the plurality of image files and the category correspondence relationship to the image display device, wherein the image display device can divide one or a plurality of image files into one or a plurality of groups according to the received classification correspondence relationship during displaying the one or the plurality of image files, and each group is corresponding to one category label. According to the technical schemes, the method has the advantages that the image files to be displayed can be adaptively classified and adjusted, and thus the image browsing speed and positioning speed can be increased.

Image processing apparatus, image processing method and program

Abstract: PROBLEM TO BE SOLVED: To allow for simple recording of RAW image data, and rapid output of an image related to the RAW image data thus recordedSOLUTION: At first, transition can be made to still image shooting mode, still image reproduction mode, video shooting mode, or video reproduction mode, as an operation mode, a captured image is developed simply and recorded on a recording medium, and RAW file is created and recorded Transition is made to an idle mode when the processing load of each mode is small, when development of high image quality is not performed, development of high image quality is performed using the RAW file thus recorded, and is replaced by an image file of simple development

System mirror image file updating method of virtual machine, cloud data center and system

Abstract: The embodiment of the invention discloses a system mirror image file updating method of a virtual machine. The system mirror image file updating method comprises the steps that when a virtual machine creating request is received, a target computing resource pool is selected from multiple computing resource pools, and a third virtual machine is created according to the target computing resource pool; on the condition that a system mirror image file of the third virtual machine is updated, data of a preset first system mother volume and incremental data of the system mirror image file are obtained; a new system mirror image file is synthesized based on the incremental data of the system mirror image file and the data of the first system mother volume; a second system mother volume of the third virtual machine is created in the target computing resource pool, and the new system mirror image file is stored in the second system mother volume; a second snapshot photo is generated according to the second system mother volume; a third linkage clone volume is created according to the second snapshot photo, and the third virtual machine is started according to the third linkage clone volume. The embodiment of the invention further discloses a cloud data center and a system. By the adoption of the system mirror image file updating method, the system mirror image file updating time is shortened, and storage space is reduced.