International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
A Survey on Various Compression Artifact Removal
Techniques
Neethu Kuriakose#1, Mr.Shanty Chacko #2
#1
#2
M. Tech Student, Department of Electronics and Communication Engineering, Karunya University, Coimbatore, India.
Assistant Professor, Department of Electronics and Communication Engineering Karunya University, Coimbatore, India.
Abstract— By the application of lossy data compression,
distortion will occur for media. This media can be image, audio,
or video. Compression artifact is one of the noticeable distortion
of media which occur as a result of lossy data compression. At
high compression ratios, the visibility of image degradations is
one of the most important drawback of the current video coding
standards. Due to the rigid block partitioning of the image, these
image degradations leads to blocking artifacts and due to coarse
quantization it leads to ringing noise mainlys around edges. Both
the blocking and ringing noise are visibly annoying and have a
great impact on the received image quality. So, for improving the
quality of the reconstructed image we must remove the blocking
and ringing noise. In some techniques, blocking noise will be
removed from the image, but there will be large amount of
blurriness. This paper is a survey on various compression
artifact removal techniques.
Keywords— Compression artifact, Blocky noise, Mosquito noise,
TV Regularization decomposition.
I. INTRODUCTION
Compression artifact is one of the most important noises
which occur due to the lossy data compression. Blocking and
ringing noise will adversely affect on the received image
quality. For overcoming this problem the most widely used
principle is low pass filtering of the decoded image in either
the temporal [1] or spatial direction [2]. Sometimes these filters
are restricted only to the block boundaries thus it can be
specifically tackle blocking noise and thus numerical
complexity can be reduced. In the version 2 of H.263 [3], a
very efficient filter of this type has been standardized and
included as optional Annex J. The main drawback of this filter
is that even though it removes much of the blocking noise, it
does not remove ringing and mosquito noise.
Some techniques enhances the decoded image by
incorporating prior knowledge about typical image data since
global smoothing for the reduction of ringing artifacts removes
the important image details. This leads to maximum a
posteriori (MAP) techniques in which Bayesian paradigm can
be used for the solution of an estimation problem involving
both a priori knowledge and the decoded image data [4], [5].
This principle is computationally very demanding since the
estimation process often involves numerical optimization of
non-convex functional.
Usually blocking and ringing noise reduction is done in the
image restoration stage after the decoding. Apart from this,
these noises can be reduced by image preprocessing at the
encoder site. This idea has been followed in [6] in which the
quantization noise of DCT coefficients is shifted to the inner
part of the block from the block boundaries. For the effective
reduction of blocking artifacts [7], some methods employ
Dolby-like noise suppression techniques. Even though a
matched receiver is required for such noise shaping for best
performance, a standardized receiver can decode an image of
reasonable quality even though it does not know about the
encoder modifications.
The technique in [8] proposes a linear low pass filter for
decreasing the blocking artifact. This filter removes the high
frequencies which are caused by blocky edges at borders. But,
the drawback in this method is that since the high frequency
components of the image were also removed, there arises the
excessive blur for the images. To the DCT coefficients of
shifted blocks, low pass filters were applied in [9]-[11]. The
techniques in [10] and [11] proposed the adaptive linear filters
to solve the problem of over-blurring of images. But the
demerit of these methods is that high computational
complexity is needed for these methods. A Projections Onto
Convex Set-based technique was introduced in [12] with
multiframe constraint sets for efficiently reducing the blocking
artifacts.
To the ringing areas, the techniques in [13] and [14] uses the
linear or nonlinear isotropic filters for the reduction of ringing
artifacts. For finding the optimal DCT coefficients which
adapts to the noise variances in different areas, the technique in
[15] introduced a noise shaping algorithm. The drawback of
these methods is that, they can only reduce ringing artifacts in
each frame. In transform domain, [16] applied the
spatiotemporal median filter for the adjacent 8  8 blocks, to
deal with the temporal characteristic of mosquito artifacts. The
lack of motion compensation and the less correlation between
DCT coefficients of the spatial adjacent 8  8 blocks in the
scheme limits the improvement in the above case.
In most of the current methods, for the reduction of the
flickering artifact, they concentrate on the reduction of
flickering artifacts in all intraframe coding. For the effective
reduction of flickering artifact, [17] proposes the quantization
error which is considered to get the optimal intra prediction
mode. In [18], they included the term flickering artifact in the
cost function for finding the block-size mode and optimal
prediction for intraframe coding. In the case of Motion JPEG
2000, for the reduction of flickering, a similar scheme is
implemented. All of these techniques are encoder-based.
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
For the efficient reduction of mosquito and flickering
artifacts which come under the temporal artifacts, temporal
correlation needs to be incorporated along with the spatial
correlation.
Section II describes the detailed explanation of various
techniques for the reduction of compression artifact.
Concluding remarks is given in section III.
II. COMPRESSION ARTIFACT REDUCTION TECHNIQUES
A. A Deblocking Method using Wavelet Transform for H.264
Mobile TV
A new deblocking method is proposed in [19] using the
wavelet transform which realizes fine deblocking performance
with low image resolution degradation. When we go through
the experimental results it is clear that a measurement value
for blocky noise, which is the GBIM [20], is lower for this
method than that of all the conventional methods. Also in the
case of subjective evaluation, high resolution images are
obtained for this method. In this technique using wavelet
transform calculation, image pixels which are decoded by the
H.264 decoder are converted into wavelet coefficients. Low
frequency band is denoted by LL and high frequency bands
are represented by LH, HL and HH in the wavelet transform.
Blocky noise concentrates in the LL band since it has a low
frequency component. Blocky noise appears in the 4  4 block
boundaries since 4  4 integer DCT is adopted in the H.264
system. Also in any macro block boundaries which has a size
larger than the 4  4 DCT blocks, there appears the blocky
noise. Also the blocky noise of the macro blocks seems to be
more dominant when compared with that of the DCT block. In
this technique, for the reduction of blocky noise, with the help
of Low Pass Filter (LPF) the macro block boundary of
wavelet coefficients in the LL band is filtered. The edges of
the image are completely removed and the image loses
sharpness if we filter all macro block boundaries. For the
effective solution of this problem, from the high frequency
bands LH, HL and HH, a masking gate signal is obtained. The
edge component has a high pixel value in the high frequency
bands. So the threshold value  is setted. When compared to
the threshold value  , if the pixel value of the high
frequency bands is higher, then the corresponding pixels in the
LL band are stopped to be filtered. By doing this signal
processing, without losing any image sharpness the block
boundaries can be removed.
Now with the help of inverse wavelet transform, the image
is decoded. Here, to block boundaries where blocky noise
appears 5 filters are applied and to all parts except block
3
boundaries, Haar filters are applied. By doing this process, it
reduces the blocky noise by maintaining the image sharpness.
B. Reduction of Ringing Noise In Transform Image Coding
Using Simple Adaptive Filter
For the effective reduction of ringing noise in transform
coded images, a simple filter is proposed in [21]. This filter is
designed specifically to change the current deblocking filter in
H.263 and also it adapts to the local image characteristics.
Both the subjective and objective image quality can be
improved by adding the proposed filter.
This filter has been placed within the prediction loop such
that the decoded and filtered image serves as reference for the
next frame to code. It has the advantage that only single frame
storage is needed for prediction as well as display.
1) Deringing Filter
Consider a motion compensated reconstructed image in the
prediction loop of H.263 after the deblocking filtering as
described in Annex J of [3] has taken place. While the
resulting image typically has only very little blocking noise
remaining at block boundaries, it does still show considerable
ringing artifacts especially towards the centre of the image
blocks. A deringing filter thus should remove this noise
without unduly destroying important high frequency image
details. This can be achieved by an adaptive lowpass filter
where the filter mask varies depending on the local image
characteristics.
TABLE 1
LOCAL 3  3NEIGHBOURHOOD CONSIDERED FOR FILTERING
g1
g2
g3
g4
g5
g6
g7
g8
g9
Consider a local 3 x 3 neighbourhood of decoded image
pixels as depicted in Table 1 having the grey levels g1 to g9.
Grey level here refers to either luminance or chrominance data.
The deblocking filter has already been applied, and the centre
pixel g5 corresponds to a block which has been coded in either
intra or interframe mode. We now replace g5 by
9


 g 5    i g i 


i 1, i 5

g 
5
  n 
(1)
where the binary switches  i are set according to
1 if g 5  g i  S
i  
0 else
and
9
n

i
(2)
(3)
i 1, i  5
The threshold S is set depending on the current
quantization parameter QP. If g5 belongs to an intraframe
coded block we set S = QP; if g5 belongs to an intercoded
block we choose S = QP/2. Parameter  controls the amount
of smoothing and typically lies in the range 8 -16.
This filter is adaptive in two ways. First, only those
neighbourhood pixels are included in the filter mask where the
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
corresponding grey level is within a certain confidence
interval around the grey level of the pixel to be filtered.
Secondly, the confidence interval itself adapts to the amount
of ringing noise expected in that the threshold S is adjusted
depending on the quantisation parameter. Finally, the filter
mask is strictly local since only pixels within a 3  3 window
around the current pixel are considered. Similar to the
deblocking filter, the deringing filter is restricted to those
blocks which have actually been coded. Pixels of non-coded
luminance or chrominance blocks are also not filtered.
Regarding the computational complexity it should be
pointed out that the  operator in eqn. 1 does not require a
multiplication but can be implemented with a simple add/not
add operation. In the worst case, therefore sixteen additions,
nine increments/shifts, and one division have to be performed
for each pixel to be filtered. It is also noteworthy that the filter
does not rely on any sequential processing and can operate in
parallel on all image pixels.
C. Iterative Procedures For Reduction Of Blocking Effects In
Transform Image Coding
A new iterative block reduction technique which is based
on the theory of projection onto convex sets as in [2] is
discussed below. Imposing a number of constraints on the
image which is coded in such a way as to restore it to its
original form which is the artifact free form. We can derive
one such constraint by exploiting the fact that corresponding
to horizontal and vertical discontinuities across boundaries of
neighboring blocks, the transform coded image which suffer
from blocking effects contains high frequency horizontal and
vertical artifacts. One step of our iterative procedure consists
of projecting the coded image onto the set of signals that are
bandlimited in the horizontal or vertical directions, since these
components are missing in the original uncoded image or
atleast can be guaranteed to be missing from the original
image prior to coding. Another constraint we have chosen in
the restoration process has to do with the quantization
intervals of the transform coefficients. Associated with
transform coefficient quantizers there are decision levels and
these decision levels can be used as the lower and upper
bounds on transform coefficients which in turn define the
boundaries of the convex set for projection. Thus onto this
convex set when we project the “out-of-bound” transform
coefficient, we will select the upper (lower) bound of the
quantization interval if its value is larger (smaller) than the
upper (lower) bound.
This paper proposes the iterative procedure. The image that
has high frequency vertical and horizontal components which
corresponds to the discontinuities of the N  N blocks is low
pass filtered or bandlimited in the first part of each iteration.
The quantization constraint is applied in the second part of
each iteration as follows. At first the image is divided into
N  N blocks and the DCT of each is taken. Then any
coefficient outside its quantization range is projected onto its
appropriate value. Under the above conditions, the Projection
Onto Convex Set theory assures that iterative projection
between S1 and S2 sets results in convergence to an element
in the intersection of the two sets.
D. A Study On Improving Image Quality Of Highly
Compressed Moving Pictures
Reduction of blocky noise using inverse wavelet transform
which is proposed in[22] is discussed below. The block size
that is generally used in JPEG and MPEG is 8 × 8 pixels.
Discontinuity is generated between the blocks, when data is
compressed, quantizing low band signals. Thus in the
reconstructed images blocky noise is included. In this method,
each block of 8 × 8 pixels is converted into 4 × 4 blocks of 2 ×
2 pixels.
1) Masking Process
Consider four bands, LL, which has both a low frequency
horizontal and vertical component, LH, which has a low
frequency horizontal component and a high frequency vertical
component, HL, which has a high frequency horizontal
component and a low frequency vertical component, and HH,
which has both a high frequency horizontal and vertical
component. To reduce the discontinuity, an LL band is filtered
and thus the blocky noise can be reduced. When we do this
process, because of filtering the edge of the images is blurry.
So masking is done which corresponds to the high frequency
components. The masking comprises of the following
processes. A threshold is set at first. If the value of the
threshold is higher when compared to the value of the high
frequency components the pixels are filtered because these
pixels are not the edge component. As a result, a clear image
is obtained since the edges are maintained. Based on the
experimental results only the threshold value is selected.
2) Inverse Wavelet Transform
The DCT coefficients are processed for 1-level inverse
wavelet transform and the reconstructed images are obtained
by adjusting the brightness. A number of filters are used in
this process for inverse wavelet transform. For reversible
transform haar filters are used in this study so that the
reconstructed images are same as those of the inverse DCT.
To remove more blocky noise, 5/3 filters which are used in
JPEG2000 are used. The threshold value is used as same as
the masking process.
3) Setting the Threshold
The blocky noise is very depending to the coding method.
The blocky noise is very visible in scenes were the camera is
moving quickly or where objects are moving fast. So, for each
type of macro blocks the threshold is set. A high threshold is
set in an intra-coding in which blocky noise occurs easily so
that the pixels are more filtered. A low threshold is set in an
inter-coding such that the pixels are filtered less. And a high
threshold is set by detecting quick camera movements. It is
possible to process two kinds of images in these processes,
those in which the camera is moving quickly and the objects
are moving fast.
4) Processing Between Frames
An MPEG sequence comprises of mainly three parts. A
series of intraframes called I-frames, a series of forward
predicted frames called P-frames, and bidirectionally
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
predicted frames called B-frames. I-frames are image frames
coded individually without any temporal prediction, P-frames
are interspersed between the I-frames, and B-frames are
interspersed between the I-frames and the P-frames. The Bframes can be considered to be motion compensated
interpolation between the P-frames and the I-frames, with the
quantizer coefficients being different in each type of frame.
The number of correlations between the frames diminishes
when we perform the masking process independently in each
frame and thus the flicker appears in the reconstructed moving
pictures. Inorder to solve this problem, the masking area and
value for P- and B-frames following an I-frame are the same
as those for an I-frame.
E. Compression Artifact Reduction Based On Total Variation
Regularization Method For MPEG-2
TV regularization method [23] which is used for the
compression artifact reduction is explained below. In this
method, with the help of TV regularization technique the input
image is mainly divided into structure component and
structure component. Consider the following function
2
(4)
E ( s)   s dxdy    i  s dxdy
The above equation is known as the ROF model for the
original TV regularization. It was proposed by Rudin, Osher,
and Fatemi. The TV regularization is a process which is used
to minimize the function given by (4). In (4), ∫| s|dxdy is a
TV term and α∫|i-s|2dxdy represents the constraint condition.
The α denotes how much the texture component is constrained
to the original input signal. In this method, first, we have the
input image as the image with artifacts. Using the TV
regularization decomposition method [24], this image is
decomposed into structure component and texture component.
The structure component comprises of smooth signals with
only very little amount of noise and edges and the texture
component comprises of noise. The blocky noise, which
occurs due to the quantization of low frequency coefficients,
and the mosquito noise, which occurs due to the quantization
of high frequency coefficients, is separated into the texture
component. The structure component gives the details of the
edge components and this is passed through the sobel filter to
extract only the edge components. The edge information is
passed to the Gaussian filter where only the edge components
are filtered to remove the mosquito noise. Now, the Gaussian
filter output is passed through a Deblocking Edge Filter (DEF)
[25] to remove the blocky noise. Finally, DEF output and
structure components are added to get the final output image
with reduced compression artifact.
III. CONCLUSION
This paper discusses about the different techniques for the
reduction of the compression artifact. The first technique is
about the deblocking method using wavelet transform for
H.264 Mobile TV. When we compare the PSNR of this
method with the deblocking filter, which is adopted in the
reference software JM for H.264 [26], we could find that the
PSNR of the wavelet method is 1dB lower. Also in this
method, delta GBIM is around zero at all bit rates. That is we
can say that, the amount of blocky noise in the proposed
method is almost the same as that in the original image. When
we compare the decoded images it is clear that in terms of
blocky noise and image sharpness the quality of the
reconstructed image in the proposed method is higher than
that in the conventional method. The second technique is
about the reduction of ringing noise in transform image
coding using simple adaptive filter. This filter is an efficient
filter to reduce the mosquito and ringing noise. This filter is
numerically simple. By varying the two parameters filter mask
and filter strength this filter adapts itself to the image content
as well as to the coding mode. Also the loop filter approach
chosen in this method allows the use of the same decoded
picture for prediction and display and so is computationally
simpler. Also it keeps the additional delay small has a slightly
better visual performance. The third technique is about the
iterative procedures for reduction of blocking effects in
transform image coding. As a result of the proposed iterative
algorithm using a 3  3 low pass filtering, the images which
are free of blockiness but have excess amount of blurriness is
obtained. The fourth technique proposes a new method of
reducing blocky noise using inverse wavelet transform for
moving pictures as a way to reduce blocky noise at high
compression rates. In this technique, a threshold value is
present for both filtering and masking process. In this paper it
was possible to save the edge deletion of filtering and also to
process images in which either the camera is moving or the
object is moving so that fine images were obtained. The fifth
technique proposes a technique for compression artifact
reduction based on total variation regularization method for
MPEG-2. When we go through the experimental results of the
objective evaluation, it is clear that, all the PSNR in the
proposed method is higher and the  GBIM is lower when
compared with all the above mentioned techniques. Also,
there is only less blocky noise and mosquito noise in the
images reconstructed using the proposed method when
compared with all the above mentioned techniques. Also, the
image quality in the proposed method is much higher than all
the above mentioned techniques.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Yan. L, “Adaptive spatial-temporal postprocessing for low bit-rate
coded image sequence”, Proc. SPIE Image and Video Processing 2,
1994, SPlE Vol. 2182, pp. 102-110.
Zakhor. A, “Iterative procedures for reduction of blocking effects in
transform image coding”, IEEE Trans. Circuits Syst. Video Technol.,
1992, 2, (l), pp. 91-95.
Draft Text of ITU-T Recommendation H.263 Version 2 (‘H.263+’),
“Video coding for low bit rate communication”, January 1998.
Ozcelik T. , Brailean J. C. , and Katsaggelos A. K. , “Image and video
compression algorithms based on recovery techniques using mean field
annealing”, Proc. IEEE, 1995, 83, pp. 304-316.
Kaup A. “Adaptive constrained least squares restoration for
removal of blocking artifacts in low bit rate video coding”, Proc. IEEE
Int. Conf. Acoustics, Speech, and Signal Processing, Munich, 21-24
April 1997, Vol. 4, pp. 2913-2916.
Girod B., Horn U., and Xiancheng Y. “Spatial shaping: A fully
compatible improvement of DCT coding”, Proc. 1993 Picture Coding
Symp., 1993, p. 3.5.
ISSN: 2231-5381 http://www.internationaljournalssrg.org
Page 337
International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
[7] Kutka. R., Kaup, A., and Hager M., “Quality improvement of low datarate compressed video signals by pre- and postprocessing”, Proc. Digital
Compression Technologies and Systems for Video Communication,
1996, Vol. SPIE-2952, pp. 42-49.
[8] T. Jarske, P. Haavisto, and I. Defee, “Post-filtering methods for reducing
blocking effects from coded images,” IEEE Trans. Cosum. Electron.,
vol. 40, no. 8, pp. 521–526, Aug. 1994.
[9]
A. Nosratinia, “Embedded post-processing for enhancement of
compressed images,” in Proc. IEEE Data Compression Conf., 1999, pp.
62–71.
[10] T. Chen, H. Wu, and B. Qiu, “Adaptive postfiltering of transform
coefficients for the reduction of blocking artifacts,” IEEE Trans.
Circuits Syst. Video Technol., vol. 11, no. 5, pp. 594–602, May 2001.
[11] S. Liu and A. Bovik, “Efficient DCT-domain blind measurement and
reduction of blocking artifacts,” IEEE Trans. Circuits Syst. Video
Technol., vol. 12, no. 12, pp. 1139–1149, Dec. 2002.
[12] B. Gunturk, Y. Altunbasak, and R. M. Mersereau, “Multiframe
blocking-artifact reduction for transform-coded video,” IEEE Trans.
Circuits Syst. Video Technol., vol. 12, no. 4, pp. 276–282, Apr. 2002.
[13] S. Oguz, Y. Hu, and T. Nguyen, “Image coding ringing artifact reduction
using morphological post-filtering,” in Proc. IEEE Int. Work.
Multimedia Signal Processing, 1998, pp. 628–633.
[14] H. Kong, Y. Nie, A. Vetro, H. Sun, and K. Barner, “Adaptive Fuzzy
Post-Filtering for Highly Compressed Video,” in Prof. IEEE Int. Conf.
Image Proc., 2004, pp. 1802–1806.
[15] S. Westen, R. Lagendijk, and J. Biemond, “Adaptive spatial noise
shaping for DCT based image compression,” in Proc. IEEE Int. Conf.
Acoustics, Speech and Signal Processing, May 1996, vol. 4, pp. 2124–
2127.
[16] S. DelCorso, C. Miro, and J. Jung, “MNR: A novel approach to correct
MPEG temporal distortions,” IEEE Trans. Consum. Electron., vol. 49,
no. 2, pp. 229–236, Feb. 2003.
[17] X. Fan, W. Gao, Y. Lu, and D. Zhao, “Flicking reduction in all intra
frame coding,” Joint Video Team of ISO/IEC MPEG and ITU-T VCEG,
JVT-E070, Oct. 2002.
[18] S. Sakaida, K. Iguchi, S. Gohshi, and Y. Fujita, “Adaptive quantization
control for reducing flicker of AVC/H.264 intra frames,” presented at
the Picture Coding Symp., Dec. 2004.
[19] T. Kariyazaki, T. Goto, S. Hirano and M. Sakurai, “A Deblocking
Method using Wavelet Transform for H.264 Mobile TV”, International
Symposium on Consumer Electronics (ICSE2009), pp.16-17, May 2009.
[20] H.R. Wu, M.Yuen, “A genaralized block–edge impariment metric for
video coding”, IEEE Signal Processing Letters, Vol.4, No.11, pp.317–
320, Nov. 1997.
[21] A. Kaup, “Reduction of Ringing Noise in Transform Image Coding
Using a Simple Adaptive Filter”, IEEE Electronics Letters, Vol.34,
No.22, pp.2110-2112, October 1998.
[22] T. Goto, T. Yamazaki and M. Sakurai, “A Study on Improving Image
Quality of Highly Compressed Moving Pictures”, International
Symposium on Information Theory and its Applications (ISITA2008),
pp.1429-1434, 2008.
[23] T. Goto, Y. Kato, S. Hirano,M. Sakurai,T. Q. Nguyen. “Compression
Artifact Reduction based on Total Variation Regularization Method for
MPEG-2”.IEEE transactions on consumer electronics, vol. 57, no. 1,
February 2011.
[24] J. Gilles, “Image Decomposition: Theory, Numerical Schemes, and
Performance Evaluation”, Advances in Imaging and Electron Physics,
Vol.158, pp.89-137, 2009.
[25] ITU-T Recommendation H.263, “Video coding for low bit rate
communication”, January 2005.
[26] Joint
Video
Team
(JVT),
“Reference
Software”,
http://iphome.hhi.de/suehring/tml/.
Neethu Kuriakose received BTech
degree
in
Electronics
and
Communication
from
Caarmel
Engineering College, Pathanamthitta,
Kerala in 2011 and pursuing MTech
in Communication Systems from
Karunya University, Coimbatore,
Tamil Nadu.
e-mail:
Mr.
Shanty Chacko received the
neethukuriakose12@gmail.com
B.E from Manipal Institute of
Technology, ME from Government
College of Technology, Coimbatore.
He is currently working as an
Assistant Professor in Karunya
University, Coimbatore. His research
interests include Image processing
and Signal processing.
e-mail: shantychacko@karunya.edu
AUTHOR’S BIOGRAPHIES
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