International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
Blocking Artifacts Reduction in DCT Image using
Fuzzy Filtering Techniques
K.Bhargavi#1, A.Sai Varun#2, D.Abhishek #3, K.pallaviteja*4
#
Student of Department of ece, Lendi institute of engineering and technology
Near Jonnada village, Vizianagaram-535005, India.
Abstract : An image consists of large data and requires more
space in the memory. The large data results in more transmission
time from transmitter to receiver. Uncompressed multimedia
(graphics, audio and video) data requires considerable storage
capacity and transmission bandwidth. However, for block based
compressed images, we face the problem of unwanted blocking
artifacts due to lack of correlation between edges of an image.
Blocking artifacts can take on several forms in DCT compressed
images. These artifacts are visually very annoying and have a
substantial impaction to the subjectively perceived image quality.
The artifacts removal algorithms are cost effective and yield
better performance in terms of both objective and subjectively.
Fuzzy filtering, dfuzzyfiltering and edge fuzzy filtering are
different filters Used to remove artifacts during decompression
phase. In this work, spatial neighboring pixels are used to deal
with blocking and ringing artifacts while temporal neighboring
pixels are utilized to remove mosquito and flickering artifacts.
Objective fidelity metrics, peak signal to noise ratio (PSNR),
Mean Square Error (MSE) and compression ratio relevant to
different images can be improved in proposed technique.
Proposed work can be implemented using Matlab 2011a version.
Keywords - Artifacts, DCT Compression, Dfuzzyfiltering, Edge
fuzzy filtering, Fuzzy filtering, Fidelity metrics (PSNR, MSE,
Compression Ratio)..
I. INTRODUCTION
Image processing has been a key prophecy for improving
image quality obtained from camera or any other source.
Many image processing techniques has been evolved.
One of the most significant techniques is image compression
which is used to reduce the amount of data required to
represent an image. The objective of image compression is to
reduce irrelevance and redundancy of the image data in order
to be able to store or transmit (text, fax, images) data in an
efficient form. Some of the finer details in the image can be
ignored for the purpose of saving a little more bandwidth or
storage space required to store an image. Compression is
classified in to 2 types
Compression
Lossless
Lossy
Lossy
b) Lossless Image Compression
a) Lossy Image Compression: It is a data encoding method that
compresses data by losing some of it. The key aim is to
minimize the amount of data that needs to be held and
transmitted by a computer .Lossy image compression is most
commonly used for compressing multimedia data such as audio,
video and still images, especially in applications such as
streaming media and internet telephony. Ideally lossy
compression is transparent or imperceptible, which can be
verified through an ABX test.
b) Lossless Image Compression: This is a class of data
compression algorithms that allows the exact accurate original
data to be reconstructed from the compressed data. The
lossless compression is used in cases where it is important that
the original and the decompressed data be identical, such as
X-ray images. Some other examples are executable programs,
text documents, and spreadsheet and source code. Some
images having file formats of PNG or GIF use only lossless
compression. It is the method mostly used in medical
applications. Lossy compression methods, especially when
used at low bitrates, introduce compression artifacts and are
especially suitable for natural images such as photographs in
applications where minor loss of fidelity is acceptable to
achieve a substantial reduction in bit rate. The process of
image compression is required in many imaging and video
applications. However, these applications are all bothered by
an annoying problem i.e. blocking artifacts.
Block based compressed signals results in blocking, ringing,
mosquito, and flickering artifacts, especially at low-bit-rate
encoding. Compressing block edges the correlation between
pixels at the border of neighboring blocks and causes blocking
artifacts individual. Ringing artifacts occur due to the loss of
high frequencies when quantizing the DCT coefficients with a
coarse quantization. Mosquito artifacts come from ringing
artifacts of compressed frames when displayed in a sequence
and becomes more annoying for blocks on the boundary of
moving object and background which has significant
Interframe prediction errors in the residual signal Flickering
artifacts occurs due to the inconsistency in quality over frames
at the same spatial position. This inconsistency is from the
temporal distortion over compressed frames caused by
quantizing the residual signal. These flickering artifacts will
be perceived more in the flat areas, also come from different
quantization levels for rate-distortion optimization.
Fig.1 compression types
a)
Lossy Image Compression
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
Many filter-based denoising methods had been proposed to
reduce these artifacts, most of which are frame-based
enhancement. In previous techniques a linear low-pass filter
was used for blocking artifact reduction, to remove the high
frequencies caused by blocky edges at borders, but excessive
blur was introduced since the high frequencies components of
the image were also almost eliminated. Low-pass filters were
applied to the DCT coefficients of shifted blocks. In this paper
we are proposing different filters like Fuzzy filter, Directional
fuzzy filter and Edge based fuzzy filters to overcome the
problem of over-blurring the images.
The boundary regions between blocks are identified as either
smooth or non-smooth regions. The blocking artifacts in
smooth regions are removed by modifying a few DCT
coefficients appropriately, whilst an edge-preserving
smoothing filter is applied to the non-smooth regions. To
reduce ringing artifacts we use the linear or nonlinear
isotropic filters in the ringing areas. For flickering artifact
removal, most of the current methods focused reducing
flickering artifacts in all Interframe coding. The quantization
error is considered to obtain the optimal intra prediction mode
and to help in reducing the flickering artifact.
II. ARCHITECTURE
The proposed architecture mainly consist of Dct function
deals mainly with separation of r, g, b planes with the help of
jpeg compression. In this proposed work our architecture is
sub divided in to 2 parts. They are
a) Discrete Cosine Transform (DCT)
b) Fuzzy Filters
a) Discrete Cosine Transform (DCT): Discrete Cosine
Transform (DCT) attempts to de correlate the image data.
After de correlation each transform coefficient can be encoded
independently without losing compression efficiency. The
Discrete Cosine Transform (DCT) is a technique that converts
a spatial domain waveform into its corresponding frequency
components as represented by a set of coefficients. The
process of reconstructing a set of spatial domain samples from
frequency components is called the Inverse Discrete Cosine
Transform (IDCT).
For data compression of image/video frames, usually a block
of data is converted from spatial domain samples to another
domain (usually frequency domain), which also offers more
compact representation. The optimal transform is the
Karhunen-Loeve Transform (KLT), as it packs most of the
block energy into a fewer number of frequency domain
elements, it minimizes the total entropy of the block, and it
completely de correlates its elements. However, its main
disadvantage is that its basis functions are image-dependent.
This complicates the digital implementation. The Discrete
Cosine Transform introduced by Ahmed in 1974, has the next
best performance in compaction efficiency, while also having
image-independent basis functions. Hence DCT is used to
provide the necessary transform and the resultant data is then
compressed using quantization and various coding techniques
to offer lossless as well as lossy compression.
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b)Fuzzy Filters: Fuzzy filters improve on median filters or
rank condition rank selection filters by replacing the binary
spatial-rank relation by a real-valued relation. The
conventional way to define the fuzzy filters is by generalizing
the binary spatial-rank relation. In this paper, the fuzzy filter is
introduced from the artifact reduction problem. Assume that a
filter is applied to a set of neighboring samples x [m + m’, n +
n’] around x [m, n] the input to form the output and its
unbiased form with normalization.
Y [m, n] =
(1)
e.q.(2)
In (1), h(x [m + m’, n + n’], x [m, n]) controls the contribution
of the input x [m +m’, n +n’] to the output. For a linear filter,
h is fixed and input-independent. In the case of a nonlinear
filter, h is a function of the input, such as for median filter.
(3)
Where round (u) is the nearest integer of u.
Due to the input independence of the filter coefficients, a lowpass filter is designed to perform effectively in the flat areas
may introduce blurring artifacts in fine detail areas. In artifact
reduction, especially for low bit-rate compression, it is
desirable to preserve the details while removing the artifacts.
This can be achieved by imposing the constraint such that if x
[m +m’, n +n’] is far from x [m, n], its contribution to the
output is small. In that case, the filter coefficients h [k, l] must
follow the constraints.
(4) &(5)
(6)
The function h(x [m + m’, n + n’], x [m, n]) is referred to as
the membership function and there are many functions which
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fulfill these requirements. For a Gaussian membership
function
(7)
Where σ represents the spread parameter of the input and
controls the strength of the fuzzy filter. Note that the
contribution of the input x [m, n] to the output is always
highest compared to the contribution of other samples
(8)
For the same, x [m + m’, n + n’] - x [m, n] the higher the
value, the higher the contribution of x [m + m’, n + n’]
relatively compared to the contribution of x [m, n] to the
output. This implies x [m, n] that will be more averaged to x
[m + m’, n + n’]. Smaller σ values will keep the signal x [m, n]
more isolated from its neighboring samples. This spread
parameter should be adaptive to different areas which have
different activity levels such as smooth or detail areas. For
multidimensional signals, the conventional fuzzy filter assigns
a fixed spread parameter for every surrounding sample and
ignores the relative position between them. In image and
video compression, artifacts such as blocking, ringing or
flickering artifacts are directional, and, thus, the fuzzy filter
should consider the directions between x[n] and its
surrounding samples x[m + m’, n + n’]. This can be achieved
by an adaptive spread parameter.
(9)
Where σ m is a position-dependent amplitude of the spread
parameter σ and k is the scaling function controlled by the
direction of x [m + m’, n + n’] to x [m, n].
Fuzzy filters are classified in to 3 types according to proposed
architecture
a) Fuzzy Logic
Early applications: The Japanese were the first to utilize fuzzy
logic for practical applications. The first notable application
was on the high-speed train in Sendai, in which fuzzy logic
was able to improve the economy, comfort, and precision of
the ride. It has also been used in recognition of hand written
symbols in Sony pocket computers; flight aid for helicopters;
controlling of subway systems in order to improve driving
comfort, precision of halting, and power economy; improved
fuel consumption for auto mobiles; single-button control for
washing machines, automatic motor control for vacuum
cleaners with recognition of surface condition and degree of
soiling; and prediction systems for early recognition of
earthquakes through the Institute of Seismology Bureau of
Metrology, Japan.
Fuzzy logic works the way that humans think as opposed to
the way that computers typically work. For example, consider
the task of driving a car. You notice that the stoplight ahead is
red and the car ahead is braking. Your mind might go through
the thought process, "I see that I need to stop. The roads are
wet because it's raining and there is a car only a short distance
in front of me. Therefore I need to apply a significant pressure
on the brake pedal." This is all subconscious (in general), but
that's the way we think - in fuzzy terms. Do our brains
compute the precise distance to the car ahead of us and the
exact coefficient of friction between our tires and the road,
and then use a Kalman filter to derive the optimal pressure
which should be applied to the brakes? Of course not. We use
common-sense rules and they seem to work pretty well. On
the other hand, when we do finally get around to pressing the
brake pedal there is some exact force that we apply, say 1.326
pounds. So although we think in fuzzy, noncrisp ways, our
final actions are crisp. The process of translating the results of
fuzzy reasoning to a crisp, non-fuzzy action is called
defuzzification.
b) Directional Fuzzy Spatial Filter: When highly
compressed, the ringing artifacts in JPEG images are
prevalent along strong edges and the filter strength should
adapt to the edge direction.
b) Directional Fuzzy Spatial Filter
c) Edge Based Directional Fuzzy Filter
a) Fuzzy Logic: In recent years, the number and variety of
applications of fuzzy logic have increased significantly. The
applications range from consumer products such as cameras,
camcorders, washing machines, and microwave ovens to
industrial process control, medical instrumentation, decisionsupport systems, and portfolio selection.
To understand why use of fuzzy logic has grown, we must
first understand what is meant by fuzzy logic.
Fuzzy logic has two different meanings. In a narrow sense,
fuzzy logic is a logical system, which is an extension of multi
valued logic. However, in a wider sense fuzzy logic (FL) is
almost synonymous with the theory of fuzzy sets, a theory
which relates to classes of objects with unsharp boundaries in
ISSN: 2231-5381
which membership is a matter of degree. In this perspective,
fuzzy logic in its narrow sense is a branch of FL. Even in its
more narrow definition, fuzzy logic differs both in concept
and substance from traditional multivalued logical systems.
In this technique we have to observe in compressed image that
in which direction the blurriness is more so to apply strong
filtering action in that direction and vice versa.. For example,
the filter should ideally apply stronger smoothing in a
direction which is having more blurriness or ringing artifacts
let us say in horizontal direction where the ringing artifacts
are likely to have no relation with the original value, and a
weaker filtering in that direction let us say it as vertical
direction, which is the edge direction of the image. One
general form of cosine-based spread parameter which satisfies
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this requirement is shown in below figure
Fig: -Angle and spread parameter for directional fuzzy filter
a) Angle, b) Spread parameter
(10)
where is the direction between the pixel of interestI[m,n]
and its surrounding pixel I[m+m’,n+n,] as shown in Figure.
is the amplitude of the spread parameter
, and are
positive scaling factors which control the maximum and
minimum strength of the directional filter. In (10)
,
attains the minimum
value in the vertical
direction and the maximum value
in the
horizontal direction. An example of the directional spread
parameter is plotted in Fig. below with
,
c) Edge Based Directional Fuzzy Filter: For real images
when we stuck with more complicated edges, the strongest
filtering is applied to the direction perpendicular to the edge.
Based on the sobel operator with horizontal and vertical
derivative approximation of the gradient.
Fig. Flow chart of the directional fuzzy filter.
To be adaptive for different areas having different activity
levels, the standard deviation STD (I [m, n]) of pixels in the
window W centered on I [m, n] is used to control the
amplitude of the spread parameter m as
III. PROPOSED TECHNIQUE
Our proposed technique consists of a flowchart and a block
diagram shown below
a) Proposed block diagram of fuzzy filtering technique
and the edges are detected by using the gradient magnitude
G=sqrt (Gx2+Gy2) Its corresponding direction is determined by
angle, θ0=atan (Gy/Gx). The spread function in this Case is
determined by the angle. (θ-θ0) instead of in θ (10), where the
angles θ and θ0 are Defined as in Figure below
Fig. Block diagram of fuzzy filtering technique
Fig. Angles and of the edge-based directional fuzzy filter.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
b) Articrafted image
Fig. Flowchart of proposed architecture
b) steps of above flowchart:
Take an input image of size m x n.
Resize it to n x n (mostly in DCT we prefer n=8) and we
get different blocks with size n x n.
c) Fuzzy filtered image
Apply Discrete Cosine Transform (DCT) to each block of
RGB plane separation.
Apply different filtering techniques like Fuzzy, Dfuzzy,
Edge fuzzy to each plane of separation and obtained
different fidelity metrics like MSE, PSNR, CR.
And also obtained fuzzy filtered output images with
minimum and maximum directions.
IV. RESULT AND ANALYIS
d) edge based fuzzy filter image when c=0
The image fidelity metrics for different input images and their
articrafted and fuzzy filtered outputs are shown below and the
output are considered in db.
Fig. Koala.jpg
e) edge based fuzzy filter image when c=1
a)
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Original image
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
Table I: Comparison Of PSNR Units In dB For Different Image Conclusion
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V. CONCLUSION
An effective algorithm for image denoising using different
filtering techniques like fuzzy filter, directional fuzzy and
edge based fuzzy filter is proposed. This novel method
overcomes the limitations of conventional nonlinear filters by
accounting for pixel’s activity and the direction between
pixels. It is shown that the proposed filtering techniques
improves image fidelity metrics like Mean square error (MSE)
and Peak signal to noise ratio (PSNR) and compression ratio
of compressed images compared to existing approaches and
improves visual quality of image. Human visual system (HVS)
should also be incorporated to evaluate the flicking artifacts
based on artifact perception for different areas and thus we
conclude that the proposed techniques are better and efficient
qualitatively and quantitatively compared to existing
techniques.
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