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Visual Colour Image Processing
G. Qiu+ and G. Schaefer++
+School of Computer Studies, The University of Leeds, UK ++School of Information Systems, The University of East Anglia, UK
Abstract
In this paper, we propose a colour image processing method by combining modern signal
processing technique with knowledge about the properties of the human colour vision system. Colour
signals are processed differently according to their visual importance. The emphasis of the technique is
on the preservation of total visual quality of the image and simultaneously taking into account
computational efficiency. A specific colour image enhancement technique, termed Hybrid Vector
Median Filtering (HVMF) is presented. Computer simulations have been performed to demonstrate that
the new approach is technically sound and results are comparable to or better than traditional methods.
Keywords: Color Image, Color Vision, HVS, Image Enhancement, Signal Processing
1.Introduction
As desktop colour equipment, such as digital cameras, scanners and colour printers are
becoming more and more readily available, colour image processing has become increasingly
important. Even though image processing has been an active research topic for more than 30 years, the
vast majority of the research has been done with respect to monochrome images. Colour images were
normally regarded as three separate monochrome images in a suitable colour space and colour image
processing was performed on separate colour planes independently.
Recently, a few image processing approaches that treated the three colour channels as an entity
have emerged. The best known perhaps is the vector median filters (VMF) developed to remove noise
from colour images [1, 2]. In VMF, a pixel is considered as a 3-d vector and images are manipulated in
an appropriate color space. Looking into the literatures, most of the authors still use RGB space for
processing, e.g. [2, 3]. Because of the poor correspondence between RGB and human visual system, it
makes sense to look for alternative colour spaces such as L*a*b* or HSI [5]. However, direct use of
colour spaces such as L*a*b* may not be appropriate in developing colour image processing strategies
because human visual system has different sensitivities to separate colour components in these colour
spaces, or equivalently, different channels conveys visual information of different importance. In order
to process colour images more effectively; properties of human visual system have to be taken into
consideration.
We attempt to develop image processing techniques suitable for enhancing visual colour
images. We use the word “visual” explicitly to stress that results of the processing are for viewing
purpose only. Human observers will be the judges of the performance of the algorithms. In this paper,
we present a visual colour image processing technique for effective noise removal. The new method
combines the theories of human colour vision system and signal processing to achieve efficiency in
computational speed and good visual image quality.
In the followings, we will first briefly describe the principle of VMF, then a new colour image
processing scheme based on VMF and the theory of human color vision will be introduced. We will
report some computer simulation results of the new method and compare it with the traditional
methods.
2.Vector Median Filtering
Vector median filtering is an extension of the scalar median filtering, it is a nonlinear filtering
technique which has been shown to give better performance than linear filtering in preserving image
edges and details. The median vector Xmv of a set of N 3-dimensional vectors W = {X1, X2..., XN},
where Xi = {xir, xig, xib}, is defined as
∑ −=∈
iijWXmv XXX
j
minarg (1)
In R-ordering implementation of VMF [3], multivariate samples are ordered according to their
distance to a pre-selected central location. The pre-selected central location can be the mean or the
marginal median of the multivariate samples.
3.Hybrid Vector Median Filtering
The opponent colour theory [4] suggests that there are three visual pathways in human colour
vision system. One pathway is sensitive mainly to light-dark variations and this pathway has the best
spatial resolution. The other two pathways are sensitive to red-green and blue-yellow variation. The
blue yellow pathway has the worst spatial resolution. In opponent colour representation, the spatial
sharpness of a colour image depends mainly on the sharpness of the light-dark component of the image
and very little on the structure of the opponent-colour image components. Given that the human visual
system has different sensitivities to different colour components, it is sensible to treat these components
differently.
In traditional VMF such as (1), each colour channel has equal weight, and this does not reflect
the way human vision system works. We propose a filtering scheme using opponent colour
representation and process the light-dark and opponent colour components separately.
Assuming a set of pixel vectors W = {X1, X2..., XN}, where Xi = {xibw, xirg, xiby} and
xibw = xir + xig + xib
xirg = xir - xig
xiby= -xir - xig + 2xib
are opponent colour representations.
The output of the hybrid vector median filtering (HVMF), Xhvm = {xmbw, xmrg, xmby}, is obtained
as follows
xmbw = median(x1bw, x2bw, …, xNbw) (2)
{ } ( ) ( )( )∑ −+−=∈
iibyjbyirgjrgWxxmbymrg xxxxxx
jybjrg
22
,minarg, (3)
The black and white component of the filtering output is obtained by calculating the scalar
median of the black and white components within the filtering window and the opponent colour
components of the HVMF output are obtained by calculating the vector median of the 2-d opponent
colour components within the filtering window.
From a computational point of view, (2) and (3) are more efficient than (1). From human visual
system perspective it also makes sense. Because we have separated chrominance from luminance, we
can process them according to their visual importance. For example, the visual sharpness of the image
is largely determined by the sharpness of the black and white components, preserving its sharpness is
most important, and nonlinear filtering such as median filter or other Order Statistic (OS) filters will be
most suitable. On the other hand, the opponent colour components have low bandwidth and the visual
sharpness of the image is not greatly affected by them, apart from using vector median filtering such as
(3), linear filters can also be applied depending on the nature of the noise. Although we use noise
removal and median filtering as a specific example, we believe the same argument, i.e. image channels
should be treated according to their visual importance, should be applied to other areas of colour image
processing as well.
4.Simulation results
We present examples of noise removal based on the method of (2) and (3). Fig.1 shows an
image contaminated by random noise whereby each channel (R, G and B) is corrupted independently
by a random noise source. The values of the noise range between -128 and +128 which are added to the
original image and data clipping was applied to make the values fall within 0 to 255; 20% of the pixels
have been corrupted. Fig. 2 shows the processed image using the HVMF of (2) and (3), and Fig. 3
shows the image processed by VMF of (1) in RGB space. Visually speaking, Fig. 2 and 3 are quite
similar, some subjects judge Fig. 2 slightly better with less objectionable noise. Figs. 4 to 6 show
results of another image. Applying the method to a large set of images indicated it worked very well.
5.Concluding Remarks Motivated by the properties of human colour vision system and modern digital signal processing
techniques, we have argued that a sensible approach to colour image processing is to combine the two
by treating the colour signals according to their visual importance. As an example, we apply the idea to
the removal of random noise in colour images. Simulation results were presented which are
encouraging. This research work is ongoing, future work should include using different filtering
strategies on black and white and opponent colour components according to the nature of the noise.
Also, proper psychovisual experiments should be set up to evaluate the influence of different
processing on different channels on the overall visual quality of the images so that visually sound and
computationally effective image enhancement techniques can be developed.
6.References
1. J. Astola, P. Haavisto and Y. Neuvo, “Vector median filters”, Proc. IEEE, vol. 78, pp. 678-689,
1990
2. K. N. Plataniotis et al, “Color image processing using adaptive multichannel filter”, IEEE Trans. on
Image processing, vol. 6, pp. 993-949, 1997
3. K. Tang, J. Astola and Y. Neuvo, “Nonlinear multivariate image filtering techniques”, IEEE Trans.
on Image Processing, vol. 4, pp. 788 - 798, 1996
4. Kaiser, P. K. and Boynton, R. M. (1996), Human Color Vision, Optical Society of America,
Washington DC
5. R. Gonzalez and R. Woods, Digital image processing, Addison-Wesley, 1993
Fig. 1 Noise contaminated Image
Fig.2 Processed image by HVMF of (2) and (3)
Fig. 3 Processed image by VMF of (1) in RGB space
Fig. 4 Noise contaminated Image
Fig.5 Processed image by HVMF of (2) and (3)
Fig. 6 Processed image by VMF of (1) in RGB space