22-02-2013, 12:21 PM
IMAGE ENHANCEMENT TECHNIQUES USING FREQUENCY DOMAIN FILTERING
[attachment=51456]
Abstract:
This paper proposes a practical
approach of uniform down sampling in image space
and yet making the sampling adaptive by spatially
varying, directional low-pass prefiltering. The high
frequency information in an image is adaptively
decreased to facilitate com- pression, The resulting
down-sampled prefiltered image remains a
conventional square sample grid, and, thus, it can
be compressed and transmitted without any change
to current image coding standards and systems. The
decoder first decompresses the low-resolution image
and then upconverts it to the original resolution in a
constrained least squares restoration process, using
a 2-D piecewise autoregressive model and the
knowledge of directional low-pass prefiltering. The
proposed compression approach of collaborative
adaptive down-sampling and upconversion (CADU)
outperforms JPEG 2000 in PSNR measure at low to
medium bit rates and achieves superior visual
quality, as well. The superior low bit-rate
performance of the CADU approach seems to
suggest that oversampling not only wastes hardware
resources and energy, and it could be
counterproductive to image quality given a tight bit
budget.
INTRODUCTION
Image enhancement techniques were studied
the proper enhancement techniques for the specific
application was found out. Various enhancement
methods were implemented. The frames captured were
enhanced using these methods and a later this was done
in real time. It was found that for acquiring large
number of frames at a faster rate Matlab to C
interfacing was required. An interface was created and
Matlab functions were called from C environment.
This inturn was used to acquire real time images. The
basic principles involved in image storage, techniques
involved in image compression were studied. The
image compression algorithms namely JPEG,
JPEG2000 and MPEG-4 were studied in detail. JPEG
algorithm was understood and implemented on image
sub blocks and on the entire image. Various aspects of
the algorithm such as effect of DC coefficient, blocking
artifacts etc was studied and implemented in real time.
The algorithm was implemented in real time in Matlab-
7 and the results analyzed. The advantages and short
comings of this algorithm were studied.The complete
algorithm of JPEG2000 was studied. The short
comings of JPEG were eliminated using JPEG2000.
The algorithm was implemented in real time in
Martlab-7.The advantages and key features of this
algorithm were studied and implemented. The tradeoffs
in both JPEG and JPEG2000 were also studied. An
equivalent C code for the JPEG algorithm was
developed and it was successfully compiled and
executed.
DOWN-SAMPLING WITH ADAPTIVE
DIRECTIONAL PREFILTERING
Out of practicalconsiderations, we make a more compact
rep- resentation of an image by decimating every other
row and every other column of the image. This simple
approach has an oper- ational advantage that the
down-sampled image remains a uni- form rectilinear
grid of pixels and can readily be compressed by any of
existing international image coding standards.
CONSTRAINED LEAST SQUARES
CONVERSION WITHAUTOREGRESSIVE
MODELING
In this section, we develop the decoder of
the CADU image compression system.We formulated
the constrained least square problem using two PAR
models of order 4 each: the model of parameters a
and the model of parameters . The two PAR
models characterize the axial and diagonal
correlations, respectively, as depicted in Fig. 4. These
two models act, in a predictive coding perspective, as
noncausal adaptive predictors. This gives rise to an
interesting interpretation of the CADU decoder:
adaptive noncausal predictive decoding constrained
by the prefiltering operation of the encoder.
[attachment=51456]
Abstract:
This paper proposes a practical
approach of uniform down sampling in image space
and yet making the sampling adaptive by spatially
varying, directional low-pass prefiltering. The high
frequency information in an image is adaptively
decreased to facilitate com- pression, The resulting
down-sampled prefiltered image remains a
conventional square sample grid, and, thus, it can
be compressed and transmitted without any change
to current image coding standards and systems. The
decoder first decompresses the low-resolution image
and then upconverts it to the original resolution in a
constrained least squares restoration process, using
a 2-D piecewise autoregressive model and the
knowledge of directional low-pass prefiltering. The
proposed compression approach of collaborative
adaptive down-sampling and upconversion (CADU)
outperforms JPEG 2000 in PSNR measure at low to
medium bit rates and achieves superior visual
quality, as well. The superior low bit-rate
performance of the CADU approach seems to
suggest that oversampling not only wastes hardware
resources and energy, and it could be
counterproductive to image quality given a tight bit
budget.
INTRODUCTION
Image enhancement techniques were studied
the proper enhancement techniques for the specific
application was found out. Various enhancement
methods were implemented. The frames captured were
enhanced using these methods and a later this was done
in real time. It was found that for acquiring large
number of frames at a faster rate Matlab to C
interfacing was required. An interface was created and
Matlab functions were called from C environment.
This inturn was used to acquire real time images. The
basic principles involved in image storage, techniques
involved in image compression were studied. The
image compression algorithms namely JPEG,
JPEG2000 and MPEG-4 were studied in detail. JPEG
algorithm was understood and implemented on image
sub blocks and on the entire image. Various aspects of
the algorithm such as effect of DC coefficient, blocking
artifacts etc was studied and implemented in real time.
The algorithm was implemented in real time in Matlab-
7 and the results analyzed. The advantages and short
comings of this algorithm were studied.The complete
algorithm of JPEG2000 was studied. The short
comings of JPEG were eliminated using JPEG2000.
The algorithm was implemented in real time in
Martlab-7.The advantages and key features of this
algorithm were studied and implemented. The tradeoffs
in both JPEG and JPEG2000 were also studied. An
equivalent C code for the JPEG algorithm was
developed and it was successfully compiled and
executed.
DOWN-SAMPLING WITH ADAPTIVE
DIRECTIONAL PREFILTERING
Out of practicalconsiderations, we make a more compact
rep- resentation of an image by decimating every other
row and every other column of the image. This simple
approach has an oper- ational advantage that the
down-sampled image remains a uni- form rectilinear
grid of pixels and can readily be compressed by any of
existing international image coding standards.
CONSTRAINED LEAST SQUARES
CONVERSION WITHAUTOREGRESSIVE
MODELING
In this section, we develop the decoder of
the CADU image compression system.We formulated
the constrained least square problem using two PAR
models of order 4 each: the model of parameters a
and the model of parameters . The two PAR
models characterize the axial and diagonal
correlations, respectively, as depicted in Fig. 4. These
two models act, in a predictive coding perspective, as
noncausal adaptive predictors. This gives rise to an
interesting interpretation of the CADU decoder:
adaptive noncausal predictive decoding constrained
by the prefiltering operation of the encoder.