Thinning based Antialiasing Approach for Visual Saliency of Digital

Images

Olivier Rukundo

Tilburg Center for Cognition and Communication, Department of Communication and Information Sciences,

Tilburg University, Warandelaan 2, Tilburg, Netherlands

Keywords: Antialiasing, ACO, Edge Detection, Thinning, Edge-matting, Compositing, Near-realism.

Abstract: A thinning based approach for spatial antialiasing (TAA) has been proposed for visual saliency of digital

images. This TAA approach is based on edge-matting and digital compositing strategies. Prior to edge-

matting the image edges are detected using ant colony optimization (ACO) algorithm and then thinned using

a fast parallel algorithm. After the edge-matting, a composite image is created between the edge-matted and

non-antialiasing image. Motivations for adopting the ACO and fast parallel algorithm in lieu of others found

in the literature are also extensively addressed in this paper. Preliminary TAA experimental outcomes are

more promising but with debatable smoothness to some extent of the original size of the images in

comparison.

1 INTRODUCTION

Reproducing faithfully a continuous signal from

digital samples remains a main concern in digital

signal processing – especially in cases where the

samples have regions with strong spatial

discontinuities or features (Rukundo, 2012;

Rukundo and Maharaj, 2014; Rukundo, Cao and

Huang, 2012; Rukundo, Wu and Cao, 2011;

Rukundo and Cao, 2012). Failure to accurately

reproduce a continuous signal leads results in

artefacts known as aliasing (Crow, 1981). In

computer vision and graphics, aliasing effect creates

visually distracting artefacts and loss of salient

details in an image. Visual saliency is a broad term

that refers to the idea that certain parts of a scene are

pre-attentively distinctive and create some form of

immediate significant visual arousal within the early

stage of human visual system (Timor and Michael,

2001). Early approaches used edge detectors to

extract such pre-attentively distinctive parts of an

object. However, today such an extraction is

increasingly also being performed by saliency

detectors, for example, for salient map construction

for assessments applications in still image and video

processing (Tong, et al, 2010; Itti, et al, 1998; Tong,

et al, 2011; Timor and Michael, 2001). Given that

salient regions of an image attract our attention, it is

very important to improve the visual quality of an

aliased salient map or contour in an image.

Therefore, much effort has been expended in

attempts to attain near-realism by developing

advanced antialiasing techniques. Near-realism is a

word used in this paper to signify the best image

approximation of reality ever achieved digitally.

With a few computationally-expensive exceptions,

most approaches based on filtering before sampling

have focused on weakening (if not to entirely

remove) the effects of image aliasing artefacts using

strategies based on creating new pixels or using

artificial or non-original information. There has been

reluctance to use non-artificial or original

information of the yet-to-be antialiased image, even

despite the well-known blurriness artefacts (which

reduce sharpness) associated with creating new or

non-original pixel values or using weighted

functions. However, an edge inferring and

smoothing antialiasing technique - defining jags as a

sequence of corner points separated by a one-pixel

width – has been proposed in (Bloomenthal, 1983)

and remains among the rarest techniques for tackling

aliasing issues by paying attention to edge

information, but this technique remains prone to

potential errors, especially at the edge vertices. It is

important to note that aliasing artefacts are easily

visible on the edges or highly discontinuous features

of an (aliased) image rendered, not on the main

658

Rukundo O..

Thinning based Antialiasing Approach for Visual Saliency of Digital Images.

DOI: 10.5220/0005356206580665

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 658-665

ISBN: 978-989-758-089-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

texture or foreground. Another edge-directed

antialiasing approach has been proposed in (Iourcha,

Yang and Pomianowski, 2009) that is technically

based on the computation of the gradients of edge

information prior to deriving filtered colors.

However, it does not scan larger edged areas or

refine potential edge pixels. Following from these

earlier attempts, a new approach based on digital

thick-edge thinning is studied and presented in this

paper. This approach begins with the premise that

the simplest way of achieving an overlay image is

via compositing – but, in this regard, the question

becomes what kind of image is to be composited or

blended for antialiasing purposes? A practical

answer to this question is very important if the

concern is the minimization or removal of the visual

aliasing artefacts via simultaneous elimination of

blurring artefacts in order to increase the sharpness

(of specific salient features) of the processed images.

Figure 1: (a) shows an aliased image of the size 256 × 256;

(b) shows an antialiased image of the size 256x256 using

the Snowbound Software.

Therefore, this paper takes care to address these

issues extensively throughout the second and third

sections, before moving on to discuss compositing in

detail. Compositing is performed between the image

with thinned edges (i.e. after the edge-matting) and

the original image or the image with aliased

artefacts. It is important to note that in order to

prevent the aliasing artefacts from occurring it is

also necessary to review the need for sampling

continuous signals as well as isotropic usage of

regular grid of pixels, but this is necessarily beyond

the scope of this paper. Figure 1 shows two images,

the aliased or original image (Figure 1 (a)) and the

image antialiased by a Snowbound Software’s

Virtual Viewer Alias/Anti-alias tool (Figure 1 (b));

in this paper, this is referred to as a traditional anti-

aliasing technique. As it can be seen in Figure 1, the

shapes of the boundaries or edges of Figure 1 (a) are

harsh; this perception of harshness (by human eyes)

is believed or inferred to be falsely represented or

aliased. In the same figure, Figure 1 (b) will appear

less harsh or smoothed at a lower resolution (i.e. the

resolution lower that 256 × 256 shown in Figure

1(b)) - compared to Figure 1 (a). Readers can use

their computer’s photo viewer to change the Figure

1 (a) and Figure 1 (b) image resolutions by zooming

out up to 10% to see how the smoothness perceived

in both cases are different or simply see how at 10%

of the Figure 1 (a) there are still image boundaries

with jagged parts, or how at 10%, the Figure 1 (b)

therefore looks smoother than Figure 1 (a)). This is

the simplest and most rapid efficiency assessment

adopted for the experimental part of this paper. By

considering the blurriness of Figure 1 (b), it becomes

apparent that surrounding stair steps or jagged edges

with intermediate shades of gray is not the most

promising way to achieve visual salience of an

image without further introducing blurriness in the

rendered image. A combination of strategies that

involve refining the actual sampled image signals

via detecting the strongest discontinuity zone (or

area) first, and then applying the thinning technique

to achieve a less harsh look, before producing the

overlay image – have been developed and applied,

and include further blurriness removal advantages.

The preliminary outcomes of this technique are

presented and discussed in this paper. Because the

history and literature around antialiasing, visual

saliency, edge detection and thinning techniques and

algorithms are already well-known and widely

documented, they will not be replicated in this

paper.

This paper is organized as follows: Part II

introduces ACO edge detection; Part III presents the

thinning and algorithm adopted; Part IV presents the

proposed TAA, Part V presents experimental

discussions, Part VI presents further discussions and

Part VII offers a conclusion.

2 ACO EDGE DETECTION

Detecting thick edges – or large image’s zones with

stronger discontinuities – is crucial in the strategy of

edge-matting. The tool adopted for this process is

the ACO (Dorigo and Stutzle, 2004; Baterina and

Oppus, 2010; Rezaee, 2008; Lu and Chen, 2008).

The traditional edge detection methods – such as

Sobel or Canny edge filter/detector – act like step

detectors in their operations, and consequently are

unable to adequately cover large enough areas of

discontinuities (Canny, 1986; Gonzalez and Woods,

2008). Unlike the ACO edge detector results shown

in Figure 3 (a), after applying these step detectors to

the image in Figure 1 (a), the obtained results

(shown in Figure 2 (a) and Figure 2 (b)) are

ThinningbasedAntialiasingApproachforVisualSaliencyofDigitalImages

659

evidence that Sobel and Canny edge detectors cover

a smaller area of image discontinuities. As it can be

observed, the boundaries of Figure 2 (a) do not have

a discernable difference in shape compared to Figure

1 (a). Under such circumstances, there is no need for

thinning – given that the single-pixel-width ridgeline

has been achieved – but without considering the

influence of potentially larger zones of image

discontinuities surrounding or adjacent to the step

edge strip detected. Also, as shown in Figure 2 (b),

the outcome of the Canny step detector technique is

unsatisfactory when applied to larger zones of image

feature discontinuities – which are crucial for

achieving antialiasing of images without introducing

new pixels or using non-artificial pixel values.

Figure 3 (a) shows the results obtained after

detecting edges of Figure 1 (a) using the ACO edge

detection method.

Figure 2: (a) edge detection using Sobel and (b) edge

detection using Canny method.

As it can be observed, the boundaries are thicker and

visibly different in shape than in Figure 2 (a) and

Figure 2 (b). This demonstrates the utility of the

ACO edge detection method for the purpose of

studying larger zones of image discontinuities

(without the need for redefining the edge or the

algorithmic parameters). Following from this, the

question becomes how to reduce the large zones

covered by ACO to a ridgeline of single pixel width?

It is important to note that the objective is to achieve

an average shape that uses only the original pixels

Figure 3: (a) shows the edges detected by ACO; (b) shows

a zoomed-in portion of the detected edge with a red line

showing the approximation of the average shape; and the

estimate location of the thinned edge ridgeline.

and reduces the visible harshness possibly induced

by the aliasing artefacts. It is from this that the need

for application of the thinning concept emerges. In

order to reduce detected edges to a single pixel

width size, it is critical to thin the ACO detected

edges. As high computational cost is another issue in

developing digital image processors, a fast thinning

algorithm has been used in this process.

3 THINNING

A process of reducing an object in a digital image to

the minimum size necessary for machine recognition

of that object is referred to as thinning (Abe,

Mizutani and Wang, 1994; Wang and Zhang, 1989;

Zhang and Suen, 1984). This consists of converting

binary shapes obtained from edge/boundary

detection to one-pixel width lines. This is based on

deleting pixels iteratively inside the shape to shrink

it without shortening it or breaking it apart. A fast

parallel algorithm based on the work done by Zhang

and Suen in (Zhang and Suen, 1984) has been used

for this purpose and the result is shown in Figure 4.

The matlab source code of this algorithm is available

at the website of matworks. As can be observed in

Figure 4, the right-angledness of the stair-steps’

vertices has been changed.

Figure 4: A portion of the edge thinned using fast parallel

algorithm for thinning developed by Zhang and Suen, in

1984.

The new image appears much more near-realistic

and with reduced harshness, an outcome achieved

via inducing these changes to every vertex.

Therefore, thinning the image edges detected by

ACO is the final step in the edge-matting strategy

and is also the most computationally expensive part

of the TAA.

4 TAA

A process of extracting the object from the original

image is often referred to as matting, while the

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

660

process of inserting the object into another image is

called compositing (Szeliski, 2010). In this paper,

the word matting has been used in connection with

the process of extracting the thinnest ridgelines and

therefore named ‘edge-matting’. A corresponding

and specifically extracted ridgeline or shape has

been referred to as ‘edge-matted’. In Figure 5, the

light blue color indicates the thickest ridgeline

detected by the ACO – or ‘thick-ridgeline’ – while a

dark blue color (crossing into the center of the thick-

ridgeline) indicates the potential final location of the

‘edge-matted’ with reference to this thick-ridgeline.

Figure 5: Example of the thick-ridgeline and edge-matted

in light and dark blue, respectively – with similar thick-

ridgelines boundaries.

As can be observed, the thick–ridgeline shape is

similar to the estimate edge-matted shape. The edge-

matted shape is inferred according to the concept of

iterative deleting of pixels side-by-side adopted by

many image thinning and skeletonization

algorithms. Referring to the results shown in Figure

2 (a) and Figure 2 (b) and the inferred result in

Figure 5, either in light or dark blue, there is a strong

similarity in shapes. This indicates that when the

thick-ridgelines boundaries are similar in shape (or

evolve at the same frequency) the resulting edge-

matted will have exactly the same shape, thus

Figure 6: Example of the thick-ridgeline and ‘copied’

edge-matted in light and dark blue, respectively – with

dissimilar thick-ridgelines boundaries.

making it difficult to antialias using the TAA

approach. However, when the thick–ridgelines

boundaries (see Figure 6) are dissimilar in shape,

consequently the inferred edge-matted will also be

dissimilar to those thick-ridgelines shapes (see

Figure 7). This phenomenon demonstrates the

importance of not ignoring larger image

discontinuity zones, especially when the concern is

to achieve the most reasonable shape of edge-

matted.

It should be noted that the ACO edge detector

was chosen due to its potential for overcoming the

limitations of traditional edge detection methods and

its ability to extract the thickest ridgeline in a more

efficient and reliable way (Baterina and Oppus,

2010).

Figure 7: Example showing approximately how iterative

deletion of spurious pixels is performed. The blueish lines

represent the edge-matted.

Figure 7 uses various colors to show approximately

how the iterative deletion of spurious pixels is

performed (from light yellow color to dark yellow

color and then to a mix of light and dark blue colors)

– and provides evidence that, under such

circumstances, the final inferred edge-matted shape

is the average shape of (and is thereby dependent

upon) the shapes of the thick-ridgeline boundaries.

This dependence is outstanding finding of this paper

and could serve to initiate a revolutionary step

forward in developing further applications. Figure 8

shows the inferred edge-matted according to the

shape of the thick-ridgeline boundaries. In Figure 8,

the shape of edge-matted is different from that in

Figure 5 and with less harsh boundaries compared to

the thick-ridgelines shapes – while in Figure 5, edge-

matted and thick-ridgelines have no dissimilarities in

shape. In addition, comparing the edge-matted shape

to that of any of the boundaries of the thick-edge, the

former looks ‘smoothly’ much better than the latter.

As these ideal cases demonstrate, the result is that

the edge-matted inferred in Figure 5 and Figure 8

have different outcomes although the thinning

ThinningbasedAntialiasingApproachforVisualSaliencyofDigitalImages

661

processes (or iteratively deletion performed side–

by–side) were the same. For antialiasing purposes,

the final compositing strategy has been divided into

two important steps, namely alpha compositing and

thresholding.

Figure 8: Example of the resulting edge-matted.

Alpha compositing was adopted as the easiest

operation of combining a non-antialiasing image

with an edge-matting image in order to create a new

image or appearance. Thresholding has been

introduced and applied to the overlay image in order

to extract (or output) the refined composite image.

Referring to compositing algebra, the over-operator

has been adopted - as it is suitable for placing an

image on the top of the other (or simply blending

two images) – and accomplished by applying

Equation (1).

)1(

abbaao

CCC



 (1)

where

is the result of the operation or composite

image,

is the colour of the pixel in edge-matted

image,

is the colour of the pixel in the original

image – and,



and



are the alpha of the pixels

in the edge-matted and original image, respectively

(Porter and Duff, 1984). Figure 9 recapitulates the

TAA and briefly illustrates the edge-matting as a

combination of edge detection and thinning. The

compositing is presented as the final strategic step in

which the overlay image is formed before applying

the threshold and outputting the results. This relative

simplicity makes the TAA approach interesting to

apply for antialiasing purposes as well as the further

development of robust antialiasing techniques. In

this approach, the original image is input and its

edges are simultaneously detected using the

algorithm developed by Tian, Yu and Xie in

(Tian,Yu and Xie, 2008) and whose matlab source

code is available on the website of matworks. This is

followed by the edge thinning process using a fast

parallel algorithm for thinning developed by Zhang

and Suen (Zhang and Suen, 1984).

Figure 9: Recapitulation of the TAA.

After, the resulting edge-matted image is blended

with the original image thus forming a composite

image. It should be noted that in Matlab a similar

process of blending two images can be achieved

with the help of the imfuse function. The obtained

blended images (see Figure 13) are then refined by

applying a threshold to the results of the

compositing operation, according to the Equation (2)

and Equation (3).





255124 

(2)





0124 



(3)

The outcomes of preliminary experiments and those

of the Snowbound Software’s Virtual Viewer

Alias/Anti-alias tool are presented and discussed in

the following part.

5 EXPERIMENTAL RESULTS

AND DISCUSSIONS

The TAA preliminary results are displayed at a

higher resolution in Figure 12 to clearly show the

visual difference output between the new TAA and

old concept based antialiasing. Figure 10 (a) shows

an original or non-anti-aliasing image with very

harsh edges. The thinnest skeleton (or edge-matted)

is shown in Figure 10 (b) with less harsh edges

compared to the former. It is important to note that

what can be seen as disconnections of edges is not a

flaw specific to the TAA method, and additionally is

not a concern of this experiment, since the TAA

output is an overlay image of two images (see Figure

13 (a) and Figure 13 (b)).

In addition, it is evident that it is not

necessary for all the edge-lines/boundaries of a

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662

Figure 10 : (a) non-anti-aliasing and (b) thinned edge

image.

non-antialiasing image to undergo antialiasing (see

and compare Figure 11 (a) and Figure 11 (b)).

Figure 11 : (a) a traditionally anti-aliased and (b) edge-

matted output images.

In this Figure 11 (a), it can be seen that the

traditional antialiasing technique applied along the

vertical and horizontal lines of the non-antialiasing

image were no longer necessary. This is another

advantage of the TAA technique as it means that

TAA does not use more computational effort to

calculate average pixel values to pad along such

lines. In the experimental results, shown in Figure

12 (c) and Figure 12 (d), a blurring layer of the

calculated average pixel values is added to the non-

antialiasing image edge boundaries. Until it would

be presented at a lower resolution, for example, at 64

x 64, such images would look smoother than images

at 256 × 256 resolution – thereby tricking the human

eyes through the appearance of the smoothed

boundaries of edges. A disadvantage of this

artificial-layer-adding based method is its

dependence on the images for its operation. For

example, see the results in Figure 12 (c) and Figure

12 (d). At the actual resolution, it can be observed

that they do not demonstrate the same level of

artefacts though processed by the same traditional

antialiasing method. In case of TAA shown in

Figure 12 (e) and Figure 12 (f), the boundaries of the

antialiased image look quite different – especially,

on the right-angled or vertices. In addition, the

sharpness is increased with TAA as compared to the

traditional technique which remains technically

deficient. Further locally objective assessments

using any image in various photo viewers or editing

software programmes also display better

performance of the proposed TAA compared to the

Snowbound Software case presented –especially

when the antialiased image size is reduced to 10% of

its original size. Technically reducing an image to a

particular percentage does not mean that should be

the size at which that image should remain for

further processing.

(a) (b)

(e) (f)

Figure 12: (a) and (b) - two non-antialiasing images, (c)

and (d) - two traditionally-antialiased images, (e) and (f) -

two TAA antialiased images.

This is usually done to increase the size of an aliased

image to a higher resolution for antialiasing

activities, and afterwards reduce it to a lower

resolution (matching the desired size), as a method

of concealing the visual artefacts induced and thus

creating a visually pleasing perception of that output

for a low resolution image. Further studies on this

non-artificial pixels-based antialiasing concept are

necessary given the increasing interest in achieving

near-realism as well as preventing aliasing artefacts

from occurring in images processed digitally. The

ThinningbasedAntialiasingApproachforVisualSaliencyofDigitalImages

663

following section further discusses the potential of

the TAA approach and offers insights into the

process of the imfuse function when it is applied

between the edge-matted and non-antialiasing

images (see Figure 13 (a) and Figure 13 (b)) just

before the threshold is applied.

6 FURTHER DISCUSSIONS AND

APPLICATIONS

There is the potential to further develop the

proposed approach and extend its application to

refining other categories of digital images which

also require visual saliency, as well as many other

cases in which effort to view the object’s near-

realism is needed.

Figure 13: (a) and (b) - overlay images obtained from the

edge-matted and original images, in each case. The false

color has been enabled by the ‘falsecolor’ command to

clearly show the boundaries between the edge-matted and

original images of the overlay images.

It should be noted that the proposed approach

enables the exclusive use of original pixels

information which is not possible with traditional

antialiasing methods – and is particularly important

when the blurriness artefacts are undesired. Figure

13 (a) and Figure 13 (b) show the overlay images of

each of the two processed images (of the capital and

small ‘A’ letter) – obtained by using only the imfuse

command function with no threshold applied.

However, the corresponding overlay images do not

automatically produce the desired visual effects and

therefore a threshold has been introduced and

applied to ensure that the widest visible rift is output

or obtained. Such a threshold has been applied to the

results of the compositing operation Equation 1

according to the Equation (2) and Equation (3)

commands. The average matlab lines

execution/processing time required to output the

composite image (ACO

average (four kernel functions with 30 steps)

= 24.51856875 sec. or ACO

average (one kernel function with 30

steps)

= 9.989709, thinning = 13.293638 sec. and

compositing = 2.085868 sec.) as well as other

computational complexity related issues will be

discussed in the optimization effort in future papers.

7 CONCLUSIONS

The results and arguments presented in this paper

concentrated on increasing or improving the visual

saliency of an image processed digitally. The

undesired phenomenon associated with digital image

processing – aliasing – results from an unfaithful

reproduction of a continuous signal or sample by a

digital processor/software. Effort to tackle this

phenomenon has led to the development of an

antialiasing method that exclusively uses non-

artificial pixels in its operations to avoid inducing

other phenomena such as blurriness or haloness,

(which also reduce the visual saliency). This TAA

approach comprises of edge-matting and

compositing strategic steps – which have been

thoroughly examined and experimental results have

been provided in the previous sections of this paper.

The key differences between the two strategies have

been explored and the preliminary experimental

results demonstrate the lack of edge vertices errors

via avoiding the averaged pixels’ padding process

and by achieving high quality edge refinement via

thinning. The quality performance measure selected

locally served as an ‘objective assessment’ as it is

possible to use any photo viewer or editing software

locally due to the simplicity of the latter. The

sharpness achieved by TAA is very promising but

the smoothness remained debatable to some extent

compared to the other case studies mentioned.

Further investigations and improvements are

expected in the future research works, especially in

terms of computational complexity and accuracy as

well as comparative evaluations using more

complicated images for testing more objects.

ACKNOWLEDGEMENTS

This work was supported Tilburg Center for

Cognition and Communication, Tilburg School of

Humanities, Tilburg University. The author would

like to thank the reviewers and editors for their

helpful comments.

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