547 research outputs found
Study and simulation of low rate video coding schemes
The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design
Virtually Lossless Compression of Astrophysical Images
We describe an image compression strategy potentially capable of preserving the scientific quality of astrophysical data, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly lossless techniques, by which moderate compression ratios are attainable, and conventional lossy techniques, in which the mean square error of the decoded data is globally controlled by users, near-lossless methods are capable of locally constraining the maximum absolute error, based on user's requirements. An advanced lossless/near-lossless differential pulse code modulation (DPCM) scheme, recently introduced by the authors and relying on a causal spatial prediction, is adjusted to the specific characteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise is preliminarily estimated to drive the quantization stage for high quality, which is the primary concern in most of astrophysical applications. Extensive experimental results of lossless, near-lossless, and lossy compression of astrophysical images acquired by the Hubble space telescope show the advantages of the proposed method compared to standard techniques like JPEG-LS and JPEG2000. Eventually, the rationale of virtually lossless compression, that is, a noise-adjusted lossles/near-lossless compression, is highlighted and found to be in accordance with concepts well established for the astronomers' community
Compression using Wavelet Transform
Audio compression has become one of the basic technologies of the multimedia
age. The change in the telecommunication infrastructure, in recent years, from
circuit switched to packet switched systems has also reflected on the way that
speech and audio signals are carried in present systems. In many applications,
such as the design of multimedia workstations and high quality audio
transmission and storage, the goal is to achieve transparent coding of audio and
speech signals at the lowest possible data rates. In other words, bandwidth cost
money, therefore, the transmission and storage of information becomes costly.
However, if we can use less data, both transmission and storage become
cheaper. Further reduction in bit rate is an attractive proposition in applications
like remote broadcast lines, studio links, satellite transmission of high quality
audio and voice over internet
A Novel Hybrid Linear Predictive Coding � Discrete Cosine Transform based Compression
Image compression is a type of data compression applied to digital images, for reducing the cost for their storage and transmission. Algorithms may take advantage of Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless. On useful technique used for lossy compression is the discrete cosine transform (DCT) that helps separate the image into parts (or spectral sub-bands) of differing importance (with respect to the image's visual quality). The DCT is similar to the discrete Fourier transform in the sense that it transforms a signal or image from the spatial domain to the frequency domain. This paper proposes a hybrid lossy compression technique using Linear Predictive Coding (LPC) and Discrete Cosine Transform (DCT) to provide superior compression ratios
Database of audio records
Diplomka a prakticky castDiplome with partical part
Secure and efficient storage of multimedia: content in public cloud environments using joint compression and encryption
The Cloud Computing is a paradigm still with many unexplored areas ranging from the
technological component to the de nition of new business models, but that is revolutionizing the way we design, implement and manage the entire infrastructure of information technology.
The Infrastructure as a Service is the delivery of computing infrastructure, typically a virtual data center, along with a set of APIs that allow applications, in an automatic way, can control the resources they wish to use. The choice of the service provider and how it applies to their business model may lead to higher or lower cost in the operation and maintenance of applications near the suppliers.
In this sense, this work proposed to carry out a literature review on the topic of Cloud
Computing, secure storage and transmission of multimedia content, using lossless compression, in public cloud environments, and implement this system by building an application that manages data in public cloud environments (dropbox and meocloud).
An application was built during this dissertation that meets the objectives set. This system provides the user a wide range of functions of data management in public cloud environments, for that the user only have to login to the system with his/her credentials, after performing the login, through the Oauth 1.0 protocol (authorization protocol) is generated an access token, this token is generated only with the consent of the user and allows the application to get access to data/user les without having to use credentials. With this token the framework can now operate and unlock the full potential of its functions. With this application
is also available to the user functions of compression and encryption so that user can make the most of his/her cloud storage system securely. The compression function works using the compression algorithm LZMA being only necessary for the user to choose the les to be compressed.
Relatively to encryption it will be used the encryption algorithm AES (Advanced Encryption Standard) that works with a 128 bit symmetric key de ned by user.
We build the research into two distinct and complementary parts: The rst part consists
of the theoretical foundation and the second part is the development of computer application where the data is managed, compressed, stored, transmitted in various environments of cloud computing. The theoretical framework is organized into two chapters, chapter 2 - Background
on Cloud Storage and chapter 3 - Data compression.
Sought through theoretical foundation demonstrate the relevance of the research, convey some of the pertinent theories and input whenever possible, research in the area. The second part of the work was devoted to the development of the application in cloud environment.
We showed how we generated the application, presented the features, advantages, and
safety standards for the data. Finally, we re ect on the results, according to the theoretical
framework made in the rst part and platform development.
We think that the work obtained is positive and that ts the goals we set ourselves
to achieve. This research has some limitations, we believe that the time for completion was scarce and the implementation of the platform could bene t from the implementation of other features.In future research it would be appropriate to continue the project expanding the capabilities
of the application, test the operation with other users and make comparative tests.A Computação em nuvem é um paradigma ainda com muitas áreas por explorar que
vão desde a componente tecnológica à definição de novos modelos de negócio, mas que está
a revolucionar a forma como projetamos, implementamos e gerimos toda a infraestrutura da
tecnologia da informação.
A Infraestrutura como Serviço representa a disponibilização da infraestrutura computacional,
tipicamente um datacenter virtual, juntamente com um conjunto de APls que permitirá
que aplicações, de forma automática, possam controlar os recursos que pretendem utilizar_ A
escolha do fornecedor de serviços e a forma como este aplica o seu modelo de negócio poderão
determinar um maior ou menor custo na operacionalização e manutenção das aplicações junto
dos fornecedores.
Neste sentido, esta dissertação propôs· se efetuar uma revisão bibliográfica sobre a
temática da Computação em nuvem, a transmissão e o armazenamento seguro de conteúdos
multimédia, utilizando a compressão sem perdas, em ambientes em nuvem públicos, e implementar
um sistema deste tipo através da construção de uma aplicação que faz a gestão dos
dados em ambientes de nuvem pública (dropbox e meocloud).
Foi construída uma aplicação no decorrer desta dissertação que vai de encontro aos objectivos
definidos. Este sistema fornece ao utilizador uma variada gama de funções de gestão
de dados em ambientes de nuvem pública, para isso o utilizador tem apenas que realizar o login
no sistema com as suas credenciais, após a realização de login, através do protocolo Oauth 1.0
(protocolo de autorização) é gerado um token de acesso, este token só é gerado com o consentimento
do utilizador e permite que a aplicação tenha acesso aos dados / ficheiros do utilizador
~em que seja necessário utilizar as credenciais. Com este token a aplicação pode agora operar e
disponibilizar todo o potencial das suas funções. Com esta aplicação é também disponibilizado
ao utilizador funções de compressão e encriptação de modo a que possa usufruir ao máximo
do seu sistema de armazenamento cloud com segurança. A função de compressão funciona
utilizando o algoritmo de compressão LZMA sendo apenas necessário que o utilizador escolha os
ficheiros a comprimir. Relativamente à cifragem utilizamos o algoritmo AES (Advanced Encryption
Standard) que funciona com uma chave simétrica de 128bits definida pelo utilizador.
Alicerçámos a investigação em duas partes distintas e complementares: a primeira parte
é composta pela fundamentação teórica e a segunda parte consiste no desenvolvimento da aplicação
informática em que os dados são geridos, comprimidos, armazenados, transmitidos em
vários ambientes de computação em nuvem. A fundamentação teórica encontra-se organizada
em dois capítulos, o capítulo 2 - "Background on Cloud Storage" e o capítulo 3 "Data Compression",
Procurámos, através da fundamentação teórica, demonstrar a pertinência da investigação. transmitir algumas das teorias pertinentes e introduzir, sempre que possível, investigações
existentes na área. A segunda parte do trabalho foi dedicada ao desenvolvimento da
aplicação em ambiente "cloud". Evidenciámos o modo como gerámos a aplicação, apresentámos
as funcionalidades, as vantagens. Por fim, refletimos sobre os resultados , de acordo com o
enquadramento teórico efetuado na primeira parte e o desenvolvimento da plataforma.
Pensamos que o trabalho obtido é positivo e que se enquadra nos objetivos que nos propusemos
atingir. Este trabalho de investigação apresenta algumas limitações, consideramos que
o tempo para a sua execução foi escasso e a implementação da plataforma poderia beneficiar
com a implementação de outras funcionalidades. Em investigações futuras seria pertinente dar continuidade ao projeto ampliando as potencialidades da aplicação, testar o funcionamento
com outros utilizadores e efetuar testes comparativos.Fundação para a Ciência e a Tecnologia (FCT
Approximate trigonometric expansions with applications to signal decomposition and coding
Signal representation and data coding for multi-dimensional signals have recently received considerable attention due to their importance to several modern technologies. Many useful contributions have been reported that employ wavelets and transform methods. For signal representation, it is always desired that a signal be represented using minimum number of parameters. The transform efficiency and ease of its implementation are to a large extent mutually incompatible. If a stationary process is not periodic, then the coefficients of its Fourier expansion are not uncorrelated. With the exception of periodic signals the expansion of such a process as a superposition of exponentials, particularly in the study of linear systems, needs no elaboration. In this research, stationary and non-periodic signals are represented using approximate trigonometric expansions. These expansions have a user-defined parameter which can be used for making the transformation a signal decomposition tool. It is shown that fast implementation of these expansions is possible using wavelets. These approximate trigonometric expansions are applied to multidimensional signals in a constrained environment where dominant coefficients of the expansion are retained and insignificant ones are set to zero. The signal is then reconstructed using these limited set of coefficients, thus leading to compression. Sample results for representing multidimensional signals are given to illustrate the efficiency of the proposed method. It is verified that for a given number of coefficients, the proposed technique yields higher signal to noise ratio than conventional techniques employing the discrete cosine transform technique
Critical Data Compression
A new approach to data compression is developed and applied to multimedia
content. This method separates messages into components suitable for both
lossless coding and 'lossy' or statistical coding techniques, compressing
complex objects by separately encoding signals and noise. This is demonstrated
by compressing the most significant bits of data exactly, since they are
typically redundant and compressible, and either fitting a maximally likely
noise function to the residual bits or compressing them using lossy methods.
Upon decompression, the significant bits are decoded and added to a noise
function, whether sampled from a noise model or decompressed from a lossy code.
This results in compressed data similar to the original. For many test images,
a two-part image code using JPEG2000 for lossy coding and PAQ8l for lossless
coding produces less mean-squared error than an equal length of JPEG2000.
Computer-generated images typically compress better using this method than
through direct lossy coding, as do many black and white photographs and most
color photographs at sufficiently high quality levels. Examples applying the
method to audio and video coding are also demonstrated. Since two-part codes
are efficient for both periodic and chaotic data, concatenations of roughly
similar objects may be encoded efficiently, which leads to improved inference.
Applications to artificial intelligence are demonstrated, showing that signals
using an economical lossless code have a critical level of redundancy which
leads to better description-based inference than signals which encode either
insufficient data or too much detail.Comment: 99 pages, 31 figure
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