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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