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Data Processing, Algorithm and Modelling
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Application specific compression for mini-satellites with limited downlink capacity
Model for content- based compression
The proposed model consists of four major processing steps, namely the image analysis to locate
application-specific ROIs, the transformation of the analysis into an abstract representation for the
compression, the actual com pres sion, and the buffering of the data for later transmission.
- Image Analysis
In the case of X-Sat the first processing step analyses the scene utilising a modified vers ion of the
ISODATA classifier with k uniformly distributed class centres as initialisation. The modi fication concerns
the computation of the class centres Ci and is based on the work of Looney (Looney, 1999), who
introduced the so- called modified weighted fuzzy expected value Vi. The advantage of this measure is
the increased insensitivity to noise in contrast to the normally used mean value. The Vi is defined
iteratively, i.e. Vi = Vi(¥) , over a set of multispectral samples {x1.........xp}
The process converges quickly and can be started with the arithmetic mean and standard deviation for
Vi(0) and si(0) , respectively. Beside the robust behaviour of the fuzzy measure a high stability and
reliability is guaranteed – it precludes convergence to local minima and prevents the precipitated
elimination of small classes in an early stage of the classification. After every fifth iteration the existing
classes are analysed according to possible merging and splitting operations. By incorporating spatial
aspects in the analysis, i.e. distribution within the image and shape of the corresponding feature, the
elimination of small classes with distinct characteristics is avoided. A more detailed description of the
algorithm was presented in (Bretschneider et al., 2002).
- Synthesis of Compression Map
The result of the depicted approach is a classification map, which can be labelled according to the
spectral characteristic of the individual classes. For this purpose an additional database provides a
reflectance library. Note that the actual utilised analysis strategy depends highly on the application in
mind. However, the general intention is the identification of relevant image content. Mathematically the
process is described by
where I is the image and C the corresponding content descriptor. The function þ is the utilised analysis
algorithm. Subsequently the compression map M is com puted based on the results C of the previous
analysis:
The compression map is the two-dimensional descriptor for the application specific interest. The actual
descriptor values are not necessarily scalars but might be multidimensional to reflect different aspects
of interest. Two possible objectives were identified:
- Spectral accuracy: Every pixel gets an accuracy-value assigned that enables controlled lossy
compres sion. Note that spectral distortion introduced by a lossy compression does not depend
on the application driven interest, but on the consecutive ground-based analysis.
- Priority: Every pixel gets a priority assigned, which determines its importance for the application
and therefore for the downlink order in case the entire image cannot be transmitted.
Further objectives are likely, whereby the breakdown of the accuracy according to the individual
multispectral band is one of the most probable extensions .
- Application- Driven Compression
In a first investigation, the joint picture expert group (JPEG) algorithm has been used, which is widely
used in many applications including onboard remote sensing coding (Pelon and Spiwack, 1996), (Hou
et al., 2000). The JPEG baseline compression scheme is a discrete cosine transform (DCT) of 8þ8 pixel
blocks. For each block the DCT coeffi cients are quantised according to a quantisation table, i.e. each
coefficient is divided by a corresponding value stored in the quantisation table. Subsequently the result
is rounded to the nearest integer * . In this paper the baseline JPEG algorithm, which uses a constant
quantisation table for the whole image, was modified similarly to the variable quality JPEG proposed by
(Golner et al., 2001) allowing a space-variant quality – in particular a different quantisation – for each
8x8 DCT block. The actual quality values were derived from the accuracy components of the
whereby N8þ8 identifies pixels that belong to the same DCT block. Therefore the application’s interest is
enforced without penalising certain pixel regions due to their neighbourhood. Note that the second
outlined objective for the compression, i.e. the prioritising of pixels, was not considered, since the
investigation focuses on the achievable quality.
- Data Buffer
The compressed data is accumulated in the data buffer and downloaded to the ground receiving station
when possible. The buffer manager schedules the transmission of packages according to the
compression maps with a high scientific value first and therefore enables the prioritising on a higher
level among different scenes. This is of particular interest since the downlink capacity is not constant,
i.e. it depends on the actual orbit. Nevertheless, this approach will download as many packages as
possible, ordered by priority and therefore adaptively transmit the maximum scientific value, which is
described in terms of the mission, through the given channel. A flowchart of the entire model is depicted
in Figure 1.
- Mapping of the Model on the Hardware Architecture
The time while X-Sat is over Singapore, can capture an image, and subsequently downlink it is limited.
Additionally the transmission within the same orbit is a system requirement, Therefore the processing
time of the data has to be kept to a minimum. Simulations have demonstrated that the simple round-
robin streaming of image stripes to individual processors will pro vide the best performance. Each strip
covers the entire swath, which results in an optimal RAM disk access and a limited communication
between nodes in the classification stage. The number of lines per strip highly depends on the utilised
analysis function þ in Equation (3) to create the compres sion map M as well as the possible latency of
the algorithm since X-Sat requires a certain time for steering from the imaging orientation to the
transmission orientation.
Figure 1: Model of the contant-based compression scheme
Results
The idea of content-based compression using an application determined compression map requires the
employment of an adaptive error measure to qualify the trade-off between accuracy and data volume.
The performance of the system cannot be measured by the signal-to-noise ratio (SNR) or the mean
square error (MSE), since these distortion measures do not compensate for the space- variant nature of
the proposed model. By definition a larger error in areas of lower interest has to be penalised less than
in regions of central significance. To reflect this concept an adaptive RMS error measure is applied to.assess the proposed compression scheme. This enables the comparison between the homogeneous
baseline JPEG compressed and the adaptive compressed image whereby it is required that both image
sets have the same final data size. Equation (6) shows the definitions for the RMS and the adaptive
RMS – called aRMS – which basically scales for each pixel pi the transmission error by the quality value
qi that is specified in the compression map.
Therefore the system performance of content- based compression is superior to homogeneous
compression if the ratio between the aRMS with constant qi and the aRMS using the content- based qi is
greater than unity for the same data volume.
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