21-05-2013, 12:37 PM
DESIGN OF M-CHANNEL PSEUDO NEAR PERFECT RECONSTRUCTION QMF BANK FOR IMAGE COMPRESSION
[attachment=54200]
ABSTRACT
This paper proposes particle swarm optimization method to design M channel near perfect reconstruction
pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest
entropy based coder. To achieve high energy compaction and least distortion, design problem is formulated
as a combination of the coding gain, low dc leakage conditions and stopband attenuation. For distortion
free signal representation perfect reconstruction and good visual quality measures are imposed as
constraints. The design problem is solved using (particle swarm optimization) PSO technique for
minimizing filter tap weights. The technique find out solution by searching feasible solutions that achieve
the best solution for the objectives criteria mentioned above. The performance of this optimization
technique in filter bank design for image compression is evaluated in terms of both objective quality via
coding gain, PSNR measures and subjective visual quality measure using both JPEG baseline image coder
and an Embedded Zerotree Wavelet (EZW) coder. For comparison same test images for approximately
same conditions and characteristics are used to measure compression ratio and peak signal to noise ratio
(PSNR) for lower bit rates.
INTRODUCTION
EZW is computationally very fast and among the best image compression algorithm known today
[1]-[2]. Multirate filter banks are an important part of the image compression since a long time.
As the time progresses research efforts are made to get more and more compression ratio with
perfect reconstruction or near perfect reconstruction. In image compression block transform and
subband coding are two most popular techniques [3]-[5]. First technique uses M channel M taps
linear phase paraunitary filter bank (DCT), while the other one use wavelets [6]. In Standard
JPEG (based on DCT) input image is transformed into decorrelated coefficients which are then
processed by a quantizer, bit allocator and entropy coder to have compression with desired quality output
image. M channel PRFB’s enjoyed success due to their low complexity with reasonable
performance. At low bit rates < (1 bpp) JPEG suffers from the blocking artifacts which can be
reduced if the no. of taps in the filter banks made higher than the no. of channels.
PSEUDO QMF BANK DESCRIPTION
A typical M-channel filter bank is shown in Figure 1. The input signal x(n) is decomposed into
M subband having equal band width, using the low-pass, bandpass and high-pass analysis filters
H0(z), H1(z),… HM-1(z) respectively. These Subband signals are down sampled by a factor of M
and processed to transmission. At the output end, the subband signals are combined after
interpolation by a factor of M and filtered via synthesis filters, F0(z), F1(z), … FM-1(z), to have
perfectly/near perfectly reconstructed output signal x (n)[11].
Coding Gain
Coding Gain In image compression designed M-band cosine modulated filter bank used as
transform should have high coding gain. As transforms with higher coding gain tend to compact
more energy into a fewer number of coefficients. Since in EZW coder progressive transmission is
there, higher coding gain almost always translates to higher image quality in the mean-squared
sense.
DESIGN ALGORITHM
In our design, we search prototype filter coefficients h that minimise the weighted combinatorial
cost function. To confine the search process in a feasible solution space, we impose a limit to the
perfect reconstruction violation measure. We have to determine only half of the coefficients since
the remaining coefficients are obtained, by applying symmetry.
Filter Banks presented in this paper are obtained from the multivariable population based
stochastic optimization technique (PSO) developed in 1995, inspired by social behaviour of bird
flocking or fish schooling [16]. It utilizes a population of particles called swarms, which fly in the
given problem space. In every iteration, each particle is updated by two values: the best solution
or fitness that has achieved so far termed pbest. Another value, called gbest obtained so far by any
particle in the population. After finding these two values, the particles adjust its velocity and
position. To initialize the algorithm, we set the coefficients to satisfy unit energy constraint. A set
of weighting factors that we have found to provide reasonable tradeoffs between various
transform properties is {10.0, 1.0, 0.5, 0.5 and 0.1}.
CONCLUSIONS
We have presented a progressive transmission image compression method where M-channel
pseudo QMF filter banks and the zerotree entropy coder are combined to get an excellent
performance in coding. The designed coder offers higher coding gain and improves visual quality
images due to finer frequency partitioning and higher energy compaction. The coder possess
advantages of both block transform (region of interest coding /decoding) and progressive image
transmission (embedded quantization, exact bit rate control, and idempotency). Finally, the both
subjective and objective quality of the system outperforms.
[attachment=54200]
ABSTRACT
This paper proposes particle swarm optimization method to design M channel near perfect reconstruction
pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest
entropy based coder. To achieve high energy compaction and least distortion, design problem is formulated
as a combination of the coding gain, low dc leakage conditions and stopband attenuation. For distortion
free signal representation perfect reconstruction and good visual quality measures are imposed as
constraints. The design problem is solved using (particle swarm optimization) PSO technique for
minimizing filter tap weights. The technique find out solution by searching feasible solutions that achieve
the best solution for the objectives criteria mentioned above. The performance of this optimization
technique in filter bank design for image compression is evaluated in terms of both objective quality via
coding gain, PSNR measures and subjective visual quality measure using both JPEG baseline image coder
and an Embedded Zerotree Wavelet (EZW) coder. For comparison same test images for approximately
same conditions and characteristics are used to measure compression ratio and peak signal to noise ratio
(PSNR) for lower bit rates.
INTRODUCTION
EZW is computationally very fast and among the best image compression algorithm known today
[1]-[2]. Multirate filter banks are an important part of the image compression since a long time.
As the time progresses research efforts are made to get more and more compression ratio with
perfect reconstruction or near perfect reconstruction. In image compression block transform and
subband coding are two most popular techniques [3]-[5]. First technique uses M channel M taps
linear phase paraunitary filter bank (DCT), while the other one use wavelets [6]. In Standard
JPEG (based on DCT) input image is transformed into decorrelated coefficients which are then
processed by a quantizer, bit allocator and entropy coder to have compression with desired quality output
image. M channel PRFB’s enjoyed success due to their low complexity with reasonable
performance. At low bit rates < (1 bpp) JPEG suffers from the blocking artifacts which can be
reduced if the no. of taps in the filter banks made higher than the no. of channels.
PSEUDO QMF BANK DESCRIPTION
A typical M-channel filter bank is shown in Figure 1. The input signal x(n) is decomposed into
M subband having equal band width, using the low-pass, bandpass and high-pass analysis filters
H0(z), H1(z),… HM-1(z) respectively. These Subband signals are down sampled by a factor of M
and processed to transmission. At the output end, the subband signals are combined after
interpolation by a factor of M and filtered via synthesis filters, F0(z), F1(z), … FM-1(z), to have
perfectly/near perfectly reconstructed output signal x (n)[11].
Coding Gain
Coding Gain In image compression designed M-band cosine modulated filter bank used as
transform should have high coding gain. As transforms with higher coding gain tend to compact
more energy into a fewer number of coefficients. Since in EZW coder progressive transmission is
there, higher coding gain almost always translates to higher image quality in the mean-squared
sense.
DESIGN ALGORITHM
In our design, we search prototype filter coefficients h that minimise the weighted combinatorial
cost function. To confine the search process in a feasible solution space, we impose a limit to the
perfect reconstruction violation measure. We have to determine only half of the coefficients since
the remaining coefficients are obtained, by applying symmetry.
Filter Banks presented in this paper are obtained from the multivariable population based
stochastic optimization technique (PSO) developed in 1995, inspired by social behaviour of bird
flocking or fish schooling [16]. It utilizes a population of particles called swarms, which fly in the
given problem space. In every iteration, each particle is updated by two values: the best solution
or fitness that has achieved so far termed pbest. Another value, called gbest obtained so far by any
particle in the population. After finding these two values, the particles adjust its velocity and
position. To initialize the algorithm, we set the coefficients to satisfy unit energy constraint. A set
of weighting factors that we have found to provide reasonable tradeoffs between various
transform properties is {10.0, 1.0, 0.5, 0.5 and 0.1}.
CONCLUSIONS
We have presented a progressive transmission image compression method where M-channel
pseudo QMF filter banks and the zerotree entropy coder are combined to get an excellent
performance in coding. The designed coder offers higher coding gain and improves visual quality
images due to finer frequency partitioning and higher energy compaction. The coder possess
advantages of both block transform (region of interest coding /decoding) and progressive image
transmission (embedded quantization, exact bit rate control, and idempotency). Finally, the both
subjective and objective quality of the system outperforms.