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segmentation tree of the block. The value of the La-
grangian cost function for each segmentation option
is then evaluated from the bottom of the tree up, and
the option with lower cost is chosen. If the decision
to segment the block using one direction is taken, the
child nodes generated in the other direction of the seg-
mentation tree are pruned. If the lowest Lagrangian
cost corresponds to a non-segmentation decision (i.e.
the block corresponds to a tree leaf), all child nodes
are pruned.
As a direct consequence of this new segmentation
scheme, the block partition dimensions become very
flexible and the method is able to adapt much more ef-
ficiently to the input signal’s features. The new flexi-
ble segmentation scheme is used by MMP-II, both for
the compression of the predicted residue and for the
prediction step. This results in a much more accurate
prediction process, creating a predicted residue with
lower energy, that is more efficiently compressed by
MMP. This partitioning method also uses block sizes
that favour the prediction process, like very narrow
blocks (e.g. 16×1), generating a more accurate pre-
diction signal.
The flexible segmentation scheme improved con-
siderably MMP’s performance. For smooth ima-
ges, MMP-FP is able to outperform state-of-the-
art transform-based algorithms for bit-rates above
0.3bpp, increasing even more MMP’s performance
for non-smooth images.
2.5 A Dictionary Training Procedure
The initial dictionary used by MMP is quite simple,
containing only a set of homogeneous blocks in each
scale, distributed along the signals’ dynamic range.
The increase of its approximation power depends on
the insertion of new code-vectors during image com-
pression. Consequently, the initial blocks are coded
in a less efficient way, due to the higher number of
segmentations imposed before the dictionary reach a
convenient variety of patterns. Therefore, a quick and
appropriate growth of the dictionary is very impor-
tant, in order to reduce the number of block segmen-
tations and, consequently, enhance the compression
performance of the algorithm. This motivated the de-
velopment of a dictionary training procedure, such
that an additional set of patterns are generated and in-
serted in the initial dictionary. A group of representa-
tive test images were encoded sequentially, at differ-
ent bit-rates, and the dictionary blocks used for coding
one image were inserted in the trained dictionary, that
was used to compress the subsequent image.
Experimental results have shown that the train-
ing procedure for smooth images increases the MMP
PSNR values by up to 0.3dB for lower bit-rates. The
use of an extra context for the initial blocks assures
that the encoder’s performance is not compromised
by the entropy increase imposed by these blocks. This
new method allowed MMP to outperform transform-
based coding algorithm for bit-rates down to 0.15bpp.
3 MMP FOR TEXT IMAGE
CODING
All previously described evolutions of MMP were de-
veloped to increase its performance for smooth im-
ages. However, the new techniques also allowed
MMP to increase its performance for non-smooth im-
ages. Furthermore, experimental results have shown
that the use of a predictive schemes is of little utility
in text images. Low pixel correlation compromise the
accuracy of the prediction stage, resulting in residue
blocks with an energy level close to that of the origi-
nal block. The cost for coding the prediction informa-
tion will be increased more than that of non-predictive
scheme compression.
This observations motivated a new implementa-
tion of the MMP algorithm, where the influence of
each previously discussed technique was studied and
evaluated, in order to obtain a new version of MMP,
specifically optimised for text-images. The resulting
encoder is not based in a predictive scheme, but uses
the features of MMP-II, as well as the flexible par-
titioning scheme, described in section 2.4. The new
method increased the MMP’s performance for text
images, with considerable computational complexity
reduction.
Such method is adequate to be used in the SCODE
application for compression of the non-smooth image
layer, obtained from the segmentation process.
4 THE SCODE APPLICATION
The SCODE software application intends to be a
stand-alonecreator a viewer of MMP documents files.
Because it is a Qt-based program (Qt is a cross-
platform application framework provided by Troll-
tech), it can run across multiple operating systems,
namely Windows, Linux/X11 and Mac OS X. This
application has been developed simultaneously with
the encoder algorithms, providing a GUI with the ba-
sic tools for image analysis and manipulation.
At this point, the application supports the display
and processing, simultaneously, of one or more im-
age files from various image formats. It also displays
A COMPOUND IMAGE ENCODER BASED ON THE MULTISCALE RECURRENT PATTERN ALGORITHM
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