The JPEG 2000 Compression Standard

Treballs finals del Màster en Matemàtica Avançada, Facultat de matemàtiques, Universitat de Barcelona, Any: 2015, Director: F. Javier Soria de DiegoNowadays digital images and videos are used in many fields of our live. From the simpler digital camera to the nets of servers in Internet, all of them work with this kind of objects. Since images are stored in these devices, we need to reduce the size of the pictures in order to accumulate as many images as possible in the minimum storage space without losing quality. This necessity has derived in the appearance of several algorithms dedicated to compress images with or without loss of data. A simple image is a matrix of points (pixels) each one indicating a level of tone or color at its spatial position. Therefore, an image can be represented mathematically as a matrix with numbers indicating the value of the pixels at each position. There exist several types of representations for images, but we are going to consider gray scale images where each pixel can take a value between 0 and 255, where the 0 value represents the black color and the 255 value represents the white color. More complex images may have several components, for instance in colored images often each pixel value is given by three components R, G, and B corresponding to its tone of red color, green color, and blue color, respectively. Before processing them, the components are decorrelated transforming them into other three components more suitable to be treated. They are also usually normalized into a symmetric range of values, normally between -1 and 1, to exploit better the capabilities of the ulterior operations. These tasks are performed by the block named Pre-Processing in Figure 1.1, where we can observe the general scheme of an image compression system

aDORe djatoka: An Open-Source Jpeg 2000 Image Server and Dissemination Service Framework

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 03:00 PM – 04:30 PMThe JPEG 2000 image format has attracted considerable attention due to its rich feature set defined in a multi-part open ISO standard, and its potential use as a holy-grail preservation format providing both lossless compression and rich service format features. Until recently there was lack of an implementation agnostic (e.g., Kakadu, Aware, etc) API for JPEG 2000 compression and extraction, and an open-source service framework, upon which rich Web 2.0-style applications can be developed. Recently we engaged in the development of aDORe djatoka , an open-source JPEG 2000 image server and dissemination framework to help address some of these issues. The djatoka image server is geared towards Web 2.0 style reuse through URI-addressability of all image disseminations including regions, rotations, and format transformations. Djatoka also provides a JPEG 2000 compression / extraction API that serves as an abstraction layer from the underlying JPEG 2000 library (e.g., Kakadu, Aware, etc).  The initial release has attracted considerable interest and is already being used in production environments, such as at the Biodiversity Heritage Library , who uses djatoka to serve more than eleven million images. This presentation introduces the aDORe djatoka image server and describes various interoperability approaches with existing repository systems.  Djatoka was derived from a concrete need to introduce a solution to disseminate high-resolution images stored in an aDORe repository system.  Djatoka is able to disseminate images that reside either in a repository environment or that are Web-accessible at arbitrary URIs.  Since dynamic service requests pertaining to an identified resource (the entire JPEG 2000 image) are being made, the OpenURL Framework was selected to provide an extensible dissemination service framework. The OpenURL service layer simplifies development and provides exciting interoperability opportunities. The presentation will showcase the flexibility of this interface by introducing a mobile image collection viewer developed for the iPhone / iTouch platform

Iris Recognition: The Consequences of Image Compression

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected

Data criticality estimation in software applications

In safety-critical applications it is often possible to exploit software techniques to increase system's fault- tolerance. Common approaches are based on data redundancy to prevent data corruption during the software execution. Duplicating most critical variables only can significantly reduce the memory and performance overheads, while still guaranteeing very good results in terms of fault-tolerance improvement. This paper presents a new methodology to compute the criticality of variables in target software applications. Instead of resorting to time consuming fault injection experiments, the proposed solution is based on the run- time analysis of the variables' behavior logged during the execution of the target application under different workloads

Approximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classification

Copyright 2019 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.This paper presents a novel approach based on the direct use of deep neural networks to approximate wavelet sub-bands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that utilizes the coarser level binary decoded wavelet sub-bands to approximate the finer level wavelet sub-bands (the image itself) through a series of deconvolutional layers. The second step aims to describe the high-level semantic content of the approximated wavelet sub- bands and to perform scene classification based on the learnt descriptors. This is achieved by: i) a series of convolutional layers for the extraction of descriptors which models the approximated sub-bands; and ii) fully connected layers for the RS image scene classification. Then, we introduce a loss function that allows to learn the parameters of both steps in an end-to-end trainable and unified neural network. The proposed approach requires only the coarser level wavelet sub-bands as input and thus minimizes the amount of decompression applied to the compressed RS images. Experimental results show the effectiveness of the proposed approach in terms of classification accuracy and reduced computational time when compared to the conventional use of Convolutional Neural Networks within the JPEG 2000 compressed domain

A SECURED AUTHENTICATED WATERMARKING TECHNIQUE

Whenever media contents transmitted through the network, compressed and encrypted media data is used so it is important to give proper protection to the data items to avoid unauthorized access and for that we need to enhance media authentication and for that the compressed encrypted media data which is used to distribute through the network is watermarked for providing proof of ownership or distributorship. For doing compression JPEG 2000 compression and while doing compression the data is packed to  low  number of bits and to this data encryption is applied so  stream cipher technique is used for avoiding media quality degradation and also this technique allow to do watermarking in a predictable manner. And a robust watermarking algorithm is used for watermarking this compressed and encrypted media data