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.