SPATIALLY CONTINUOUS ORIENTATION ADAPTIVE DISCRETE PACKET WAVELET
DECOMPOSITION FOR IMAGE COMPRESSION
Nagita Mehrseresht and David Taubman
The University of New South Wales, Sydney, Australia
In this paper, we propose an orientation adaptive discrete wavelet
transform (DWT) with perfect reconstruction. The proposed trans-
form utilizes the lifting structure to effectively orient the 2D-DWT
bases in the direction of local image features. A shifting operator
is employed within each lifting step to align spatial geometric fea-
tures along the vertical or horizontal directions. The proposed ori-
ented transform generates a scalable representation for the image
and the orientation information. To approximate the asymptotically
optimal rate-distortion performance of a piecewise regular function
more closely, we adopt a packet wavelet decomposition. The exper-
imental results obtained by implementing the proposed transform
in a JPEG2000 codec illustrate superior compression performance
for the oriented transform with more than 2?5 dB improvement for
highly oriented natural images. More importantly, even at the same
PSNR, the proposed scheme reduces the visual appearance of the
Gibbs-like artifacts significantly, considerably improving the visual
quality of the reconstructed image.
The wavelet transform has become a popular tool for image and
video compression as it generates a sparse and multiscale represen-
tation of the image. Using the discrete wavelet transform (DWT) to-
Moreover, unlike block based transform, the DWT does not suffer
from boundary artifacts in the presence of quantization noise.
Conventional two dimensional (2D) subband transforms are
formed by applying a one dimensional (1D) transform separately
along the horizontal and vertical directions. While wavelets are
adept in dealing with point-based singularities, they do not neces-
sarily provide a compact representation of edges and higher order
singularities. Natural images, on the other hand, commonly contain
regions with geometric regularities which can be approximated as
linear edges on a local level. These local edges are generally neither
vertical nor horizontal and result in considerable energy in highpass
subbands. In addition, at low bit-rates, quantization effects appear as
Gibbs-like artifacts at such edges. This jagged and ringing appear-
ance of the edges is visually disturbing.
Significant efforts have been invested in the past to adapt the
transform basis to the geometrical regularity of the image. Taubman
and Zakhor  proposed a scheme in which the image is first re-
sampled before the subband transform. The invertible re-sampling
process is performed on a block by block basis with the aim of
aligning local image edges with the vertical or horizontal directions.
A de-blocking function is also applied at block boundaries to re-
duce the appearance of blocking artifacts in the reconstructed im-
age. Wang et al.  later used similar ideas, proposing overlapped
extensions to prevent boundary artifacts. Bandelet  also aims to
capture the geometrical image regularity, but it fails to provide a
multiscale representation of the image. Second generation discrete
bandelets, proposed by Peyre and Mallat  generate a multiscale
representation of the image by applying a geometrically adaptive
bandelet filter to conventional wavelet coefficients. The proposed
bandelet filter partitions each resolution intoblocks and applies extra
decomposition stages along the image singularities. Ding et al. 
proposed a scheme to incorporate directional spatial prediction into
the conventional lifting-based wavelet transforms. Their proposed
scheme, however, does not treat predominantly vertical or horizontal
edges similarly. If the vertical DWT is performed before horizontal
decomposition, the proposed scheme fails to properly exploit geo-
metric regularities of the image along the predominantly horizontal
edges. This is because, despite the high amount of energy in the
vertically highpass subband, directional spatial prediction is applied
only within the vertically lowpass subband, without using highpass
For video compression, Ohm  and Taubman and Zakhor 
proposed the idea of aligning geometrical features between frames
through motion compensation, before applying a temporal trans-
form. Secker and Taubman  and Pesquet-Popescu and Bottreau
 later proposed a framework for motion compensated temporal
transformation, using a lifting realization of the DWT where motion
compensation is applied within the lifting steps. To avoid blocking
artifacts, inband motion compensation has also been explored for
wavelet based video compression.
In this paper we employ the idea of lifting-based spatially adap-
tive DWT for image compression. The proposed scheme uses the
lifting structure to effectively orient the 2D DWT basis functions in
the direction of local image features. Motivated by work on motion
compensated lifting for video, we employ a shifting operation within
each lifting step. Doing so the DWT is effectively applied along the
desired direction. For thefirst stage of DWT (e.g., vertical decompo-
sition) shifting can be directly applied to the baseband image signal.
For the second stage (horizontal decomposition) we use an inband
Importantly, within a region with constant orientation, the pro-
posed scheme is essentially the same as applying the conventional
DWT to a skewed version of the original image. At the boundaries
between regions with different orientations, however, transitions are
performed in the subband domain, allowing the synthesis wavelet
kernel to smooth out possible boundary artifacts.
To generate wavelet basis which approximates the asymptotically
optimal rate-distortion performance of a piece-wise regular function
more closely, we adopt a packet wavelet decomposition so as to fur-
ther decompose the subbands with high energy along the direction
of geometric flow.
Details of the proposed oriented transform and the desired packet
decomposition are given in the Sections 2 and 3. Section 4 describes
15931424404819/06/$20.00 ©2006 IEEEICIP 2006
Fig. 1. An illustration of the sample shifts required to align the ver-
tical DWT basis to the orientation of an edge.
tion information. Section 5 experimentally investigates the superior
performance of the proposed orientation adaptive wavelet transform.
Concluding remarks are given in Section 6.
2. ORIENTATION ADAPTIVE DWT
Without loss of generality, we assume that the 2D DWT is imple-
mented by vertical, followed by horizontal decomposition. We use
the notation x?to represent the vector containing the ?throw. The
conventional vertical 5?3 DWT can be implemented by the follow-
ing lifting steps,
pass vertical subbands, respectively.
Fig. 1 illustrates a line with close to vertical orientation. Using
the conventional vertical DWT generates significant activity in the
highpass subband as the transform basis is not aligned to the ori-
entation of the underlying geometric feature. Motivated by motion
compensated lifting, we employ a shifting operator within each lift-
ing step to align features in the vertical direction. We use the no-
tation W???(x?) for the shifting operator which aligns features in
x?with those in x?. The proposed oriented lifting steps can then be
?correspond to the ?throw of the high and low
So long as the shifting operator W??? only uses information from
x?, the overall transform can be trivially inverted regardless of the
actual implementation of W; in this paper we use a windowed Cubic
Spline interpolator with1
and kernels can also be used within the proposed oriented DWT.
An ad-hoc extension of the proposed scheme for near to horizon-
tal directions is to follow the equations (1) and (2) and replace x?
with a vector y?which contains the vertical subband coefficients at
column ?. However, we cannot apply a baseband shifting operator,
thpixel accuracy. For sure, other precision
W, on the vertical subband samples. Applying a linear phase shift to
highpass subband coefficients does not shift the corresponding base-
band signal. The baseband shifting function W, thus, needs to be re-
placed by an inband shifting operator. One solution could be to syn-
thesize each highpass subband (perhaps by choosing the other sub-
band to be zero) and shift the baseband signal, followed by subband
decomposition to the desired subband; however, as shown in ,
due to unavoidable frequency aliasing in subband transforms, shift-
ing a signal, generally causes frequency leakage between subbands.
This information leakage occurs where shifting both the low and the
high pass subbands. Consequently, using the above-mentioned ad-
hoc inband shifting operator, we cannot fully adapt the horizontal
transform to the desired orientation.
To efficiently align the DWT basis along both near-to-horizontal
and near-to-vertical orientations, the inband shifting operator must
essentially have same performance as shifting the baseband signal.
In particular, the order by which we implement each of the hori-
zontal and vertical decomposition must have no effect on the overall
oriented 2D transform.
We employ an inband shifting operator which exploits all the
available information, i.e., both of the high and low pass subbands.
 proposes a similar shifting operator for inband motion compen-
sated temporal decomposition. The inband shifting kernel,?
and is a composition of the subband synthesis filter with a baseband
shifting kernel and the subband analysis filter. Using y?to represent
the vector containing the interleaved vertically low and high pass
coefficients at column ?,?
features at column ?. Using these notation, the oriented horizontal
decomposition can be implemented as
Similarly, the horizontal decomposition can trivially be inverted by
reversing the order of lifting steps and replacing the summation with
uses vertically high and low pass subband coefficients at column ??
W???(y?) is essentially the same as shift-
ing the baseband signal at column ? and aligning it with geometrical
3. IMPORTANCE OF PACKET DECOMPOSITION
The orientation adaptive transform aims to align geometrical fea-
tures along vertical and horizontal directions. Even a perfectly ver-
tical (horizontal) edge, however, generates significant activity in the
horizontally (vertically) highpass subband, LH (HL). To generate a
wavelet basis which approximates the asymptotically optimal rate-
we use a packet wavelet decomposition and further decompose the
subbands with high energy along the local edges. Theoretically, by
further decomposing the LH or HL subband one must be able to rep-
resent an ideal edge with a reduced number of non-zero coefficients.
The HH subband corresponds to diagonal features. The orienta-
tion adaptive transform reduces the energy in the HH subband. De-
pending on the orientation of the edge, however, the oriented trans-
form localizes the energy in either the HL or the LH subband. We
compositiontotheHLand LHsubbands, respectively. While, gener-
ally, only one of these subbands has high energy, we persistently ap-
ply the packet decomposition to both subbands; this avoids disconti-
nuity in the wavelet transform at the transition between regions with
4.734.73 Oriented with
422.16 422.16Non oriented
Mean square Mean square
Energy of LHH
Energy of LHH
4.594.59 Oriented with
Mean squareMean square
Energy of HLH
Energy of HLH
wavelet decomposition with and without information borrowing.
The dotted arrows illustrate the source of lowpass information bor-
rowed for oriented packet decomposition.
Mean square highpass energy after one level of packet
predominantly horizontal and vertical orientation. We can, there-
fore, utilize the same packet subband coder as in JPEG2000-part II
standard to scalably code the subband coefficients generated by the
proposed oriented transform.
Theoriented packetdecomposition canbeimplemented similar to
equations (1)-(2) or (3)-(4). Importantly, inband shifting is required
for both the vertical and the horizontal decomposition as the trans-
form is being applied on the primary highpass subbands. We should,
however, note that for oriented packet decomposition, the inband
shifting operation borrows corresponding lowpass information from
the LL subband of the primary Mallat decomposition. In this paper,
for the packet wavelet decomposition, we use the same orientation
information as the primary Mallat decomposition; to compensate for
subsampling, however, the shift values (and correspondingly the ori-
entation information) are scaled by a factor of 2.
Fig. 2 presents an experiment illustrating the importance of the
information borrowing for inband shifting. Without borrowing low-
pass information, we cannot fully adapt the transform to the spatial
features in the HL or LH subband. This experiment also confirms
the analogous performance of the oriented transform in adapting to
near to vertical or near to horizontal orientations.
4. ORIENTATION ESTIMATION AND CODING
Most of the currently well developed schemes for local orientation
estimation are based on the gradient estimation. The compression
gain, however, highly depends on the energy in the highpass sub-
bands. In this paper we develop an ad-hoc scheme to estimate the
local orientations . The proposed scheme aims to minimize the total
highpass energy and compacting it into a reduced number of sam-
The orientation estimation scheme compares the total highpass
form of Section 2. At each spatial location, the direction of geomet-
ric flow is estimated by choosing the orientation which generates the
smallest total highpass energy. To smooth out the effect of noise and
avoid fluctuation in the estimated orientation, we measure the local
highpass energy for each 4 × 4 block of the baseband image. We
Conventional MallatConventional Mallat
Oriented Mallat Oriented Mallat
Conventional PW Conventional PW
Oriented PW Oriented PW
Fig. 3. The PSNR(dB) of scalably reconstructed Barbara using the
conventional and the oriented DWT, with and without the packet de-
found that using a weighted averaging function on block energies is
also beneficial for generating a more coherent orientation map of the
To efficiently code the orientation information, we use multiscale
quad-tree coding with Lagrangian based pruning. At each level, the
pruning algorithm measures the cost of coding the updated orienta-
tion values and the corresponding reduction in the highpass energy
and decides whether or not it is feasible to merge the blocks.
In this paper, we generate separate orientation field for each spa-
tial resolutions. Four levels of pruning are used and the total cost
of sending the orientation information is usually as low as 0?02 bpp;
therefore, further exploiting the correlation between orientation val-
ues at different resolution levels does not seem to be advantageous.
5. EXPERIMENTAL RESULTS
In this Section, we experimentally investigate the compression ef-
ficiency and the reconstructed visual quality of the proposed orien-
tation adaptive wavelet transform. We adopt the proposed oriented
transform into a JPEG2000 codec which supports the part-II arbi-
trary decomposition styles.
Fig 3 illustrates the PSNR of the reconstructed standard test im-
age “Barbara” (gray scale, 512×512) when scalably reconstructed
at different bit-rates using 5 levels of 5?3 DWT decomposition. We
use four different wavelet transforms in this experiment; the conven-
tional transform uses Mallat decomposition for all levels and is the
same as compressing using the JPEG2000 codec with 5?3 kernels;
the conventional packet wavelet employs one level of non-oriented
packet decomposition on the LH and HL subbands at the two finest
resolution levels; a JPEG2000 codec which supports the part-II fea-
tures can be directly used to provide such a decomposition structure;
the oriented case replaces the conventional 5?3 DWT with the pro-
posed oriented transform in the two finest resolution level; finally,
the oriented packet wavelet scheme employs the proposed oriented
transform as well as the oriented packet decomposition on the HL
and LH subbands in the two finest resolution levels. Fig. 4 indicates
the results of a similar experiment using a 512 × 512 block of the
standard test image “Bike” (gray-scale,2048 × 2560).
As shown in Figs. 3 and 4, the oriented transform outperforms
0.20.30.4 0.6 0.91.2
Conventional Mallat Conventional Mallat
Oriented MallatOriented Mallat
Conventional PW Conventional PW
Oriented PW Oriented PW
Fig. 4. PSNR results using subband transforms as in Fig. 3.
Fig. 5. Visual comparison at a same PSNR. left: Conventional DWT.
Right: Oriented DWT
the conventional DWT by more than 1 dB in “Barbara” and 2?5 dB
in the highly oriented part of the “Bike” image. Using the prun-
ing algorithm of Section 4 reduces the cost of sending orientation
information to 0?02 and 0?04 bpp for Figs. 3 and 4, respectively.
Simulation results obtainedusingother testimages alsorevealasim-
ilar behavior. The oriented transform outperforms the conventional
wavelet decomposition by up to 0?8 dB with full resolution “Bike”,
0?5 dB with “Lena” (512 × 512) and 0?8 dB with “Cameraman”
(256 × 256) standard test images. Due to unavoidable frequency
aliasing in subband transforms, employing the proposed oriented
transform in coarser resolution levels (i.e., more than 2) reveals only
a minor improvement.
Our experimental investigations show that, the effective suppres-
sion of Gibbs-like artifacts for diagonal edges provides a further in-
centive for using the proposed orientation adaptive transform. Fig.
5 compares the performance of the conventional JPEG2000 codec
(left) and the proposed oriented transform (right) when reconstruct-
ing at a same PSNR; the reconstructed image using the oriented
transform has substantially superior visual quality even though the
PSNR value is the same.
The proposed orientation adaptive transform generates a perfectly
reconstructable scalable representation of the images. The proposed
scheme utilizes the inband shifting technique and the lifting real-
ization of subband transforms to spatially adapt each lifting step to
the direction of local image edges. Packet decomposition is also
used to further decompose the LH and HL subbands and is shown
to be more effective with the orientation adaptive transforms. The
proposed scheme can be used with any wavelet kernel with lifting
realization; however, the inband shifting operator employed dur-
ing packet wavelet decomposition is more effective when used with
wavelet kernels with single predict and update steps. In this pa-
per, we have only reported results for 5?3 DWT. We, however, have
adopted the proposed orientation adaptive transform also with 9?7
kernels. While the experimental results with Mallat decomposition
are similarly promising, the oriented packet wavelet decomposition
does not yield significant improvement, when used with 9?7 DWT.
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