A contextual lightweight arithmetic coder is proposed for lossless compression of medical imagery. Context definition uses causal data from previous symbols coded, an inexpensive yet efficient approach. To further reduce the computational cost, a binary arithmetic coder with fixed-length codewords is adopted, thus avoiding the normalization procedure common in most implementations, and the probability of each context is estimated through bitwise operations. Experimental results are provided for several medical images and compared against state-of-the-art coding techniques, yielding on average improvements between nearly 0.1 and 0.2 bps.
Multicomponent data have become popular in several scientific fields such as forest monitoring, environmental studies, or
sea water temperature detection. Nowadays, this multicomponent data can be collected more than one time per year for the
same region. This generates different instances in time of multicomponent data, also called 4D-Data (1D Temporal + 1D
Spectral + 2D Spatial).
For multicomponent data, it is important to take into account inter-band redundancy to produce a more compact representation
of the image by packing the energy into fewer number of bands, thus enabling a higher compression performance.
The principal decorrelators used to compact the inter-band correlation redundancy are the Karhunen Loeve Transform
(KLT) and Discrete Wavelet Transform (DWT). Because of the Temporal Dimension added, the inter-band redundancy
among different multicomponent images is increased.
In this paper we analyze the influence of the Temporal Dimension (TD) and the Spectral Dimension (SD) in 4D-Data in
terms of coding performance for JPEG2000, because it has support to apply different decorrelation stages and transforms to
the components through the different dimensions. We evaluate the influence to perform different decorrelators techniques
to the different dimensions. Also we will assess the performance of the two main decorrelation techniques, KLT and DWT.
Experimental results are provided, showing rate-distortion performances encoding 4D-Data using KLT and WT techniques
to the different dimensions TD and SD.
This work addresses the transmission of pre-encoded video containing meteorological data over JPIP. The primary requirement
for the rate allocation algorithm deployed in the JPIP server is the real-time processing demands of the application.
A secondary requirement for the proposed algorithm is that it should be able to either minimize the mean squared error
(MMSE) of the video sequence, or minimize the maximum distortion (MMAX). The MMSE criterion considers the
minimization of the overall distortion, whereas MMAX achieves pseudo-constant quality for all frames.
The proposed rate allocation method employs the FAst rate allocation through STeepest descent (FAST) method that
was initially developed for video-on-demand applications. The adaptation of FAST in the proposed remote sensing scenario
considers meteorological data captured by the European meteorological satellites (Meteosat). Experimental results suggest
that FAST can be successfully adopted in remote sensing scenarios.
Hyperspectral images used in remote sensing can reach hundreds of megabytes due to the large number of components and
high spatial and bit-depth resolution. When these images have to be transmitted, interactive transmission is necessary to
deliver only those portions of the image that the client has requested. In such a scenario, compression is a useful tool to
reduce the required amount of network bandwidth.
JPEG2000 is a powerful image and video coding standard that, among other features, provides scalability by spatial
location, component, quality, and resolution. This is used by the JPEG2000 Interactive Protocol (JPIP) to enable the
interactive transmission of imagery. One of the most important aspects of a JPIP server is the rate-control algorithm used
to select the portions of the compressed code-stream that will be delivered to client.
The purpose of this research is to introduce new rate-control methods for the JPIP server aimed to achieve optimal
performance. We focus our attention in the adaptation of two rate-control methods developed for the JPEG2000 coder
and decoder. Experimental results suggest that both methods significantly improve coding performance without penalizing
The size of images used in remote sensing scenarios has constantly increased in the last years. Remote sensing images
are not only stored, but also processed and transmitted, raising the need for more resources and bandwidth. On another
side, hyperspectral remote sensing images have a large number of components with a significant inter-component redundancy,
which is usually taken into account by many image coding systems to improve the coding performance. The
main approaches used to decorrelate the spectral dimension are the Karhunen Loeve-Transform and the Discrete Wavelet
This paper is focused on DWT decorrelators because they have a lower computational complexity, and because they
provide interesting features such as component and resolution scalability and progressive transmission. Influence of the
spectral transform is investigated, considering the DWT kernel applied and the number of decomposition levels.
In addition, a JPIP compliant application, CADI, is introduced. It may be useful to test new protocols, techniques, or
coding systems, without requiring significant changes on the application. CADI can be run in most computer platforms and
devices thanks to the use of JAVA and the configuration of a light-version, suitable for devices with constrained resources.
Remote Sensing and Geographic Information Systems applications are becoming an important issue in research projects, territorial management and many fields of our society. These applications present some special necessities and requirements, using high resolution and hyperspectral images. The huge size of these images implies high computational resources for their processing, storage and, in some cases, high bandwidth channels for their transmission. These disadvantages can be compensated with the use of compression techniques with the capacity to widely reduce this amount of information. Recently, some image compression schemes have been used to develop novel standards and proprietary formats. ECW, MrSID, and JPEG2000 are some of them, presenting advanced features and capabilities that can be used for Geographic Information Systems to extend their functionalities. This work addresses two main topics: first, a review of the most common formats used in Remote Sensing and Geographic Information Systems environments is provided. Secondly the JPEG2000 standard is briefly explained and J2K is presented. J2K is a novel JPEG2000 implementation that allows an easy extension and modification of some coding parameters of the standard, so that an improvement of the compression performance may be achieved for some particular images in GIS scenarios.
A few tools and libraries do support today encoding and decoding JPEG2000, but only some of them are open source,
meaning that their modification is allowed. Even though the JPEG2000 standard is oriented towards the design of efficient
coders, the conceptual flexibility in the new JPEG2000 produces, in some cases, complex and difficult to understand
J2K is a novel implementation of Part 1 of JPEG2000 standard. The main motivation in this development is to generate
a completely modularized scheme where each module works independently and, in order to understand it better, all modules
have the same skeleton and only basic programming language tools are used. The main advantage of these independent
modules is that one module can be replaced without compromising the others, easing the testing of new ideas, the extension
on some operations, and even the replacement of some coding operations.
J2K provides a good basis to test and develop new ideas inside the JPEG2000 standard. Some new features are being
incorporated to J2K to extend its functionalities and to better fit in some scenarios. Numerical results are provided to
validate J2K and to compare it with some other major JPEG2000 implementations and competitive coding techniques.