Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 5302957, 11 pages
http://dx.doi.org/10.1155/2016/5302957

Research Article

An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps

Hyun Jun Park,¹ Kwang Baek Kim,² and Eui-Young Cha¹

¹Department of Computer Engineering, Pusan National University, Busan 609-735, Republic of Korea
²Department of Computer Engineering, Silla University, Busan 617-736, Republic of Korea

Received 6 September 2015; Revised 9 November 2015; Accepted 18 November 2015

Academic Editor: Toshihisa Tanaka

Copyright © 2016 Hyun Jun Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method.

1. Introduction

24-bit images can represent up to 16,777,216 colors. A variety of colors in an image provides the advantages of having greater abilities of expression and aesthetics. However, a variety of colors becomes a serious problem for most color image processing. Therefore, research to represent the various colors within a limited number of colors, which is called color quantization, has been conducted.

Self-Organizing Map (SOM), which is one of the most effective color quantization methods, provides excellent results [1–5]. However, low-frequency colors in the original image tend to be excluded during the learning process because of the characteristics of the SOM learning algorithm [6]. In addition, when SOM updates the winner node, it also updates neighbor nodes, which causes loss of the original color of the neighbor node. In particular, the loss of original color increases when SOM uses a small number of nodes to quantize the image with a small number of colors. Therefore, to complement these SOM disadvantages, we propose an effective color quantization algorithm that is effective even when it uses only a small number of colors by using an octree color quantization method.

Usually, mean absolute error (MAE), mean square error (MSE), and processing time are used to quantify the performance of the color quantization results. MAE and MSE are calculated by the following, respectively:

where denotes the original image, denotes the quantized image, and and denote image height and width, respectively.

2. Octree Color Quantization

An octree is one of the image-dependent methods classified as a hierarchical clustering method. It is faster than partitional clustering methods, but its performance is relatively poor [7, 8].

An octree is a tree structure with up to eight nodes as children. The octree can represent all colors in an image within an eight-level tree because the colors are represented with eight bits. At first, color distribution in the image is represented using an octree, which then prunes the nodes until nodes remain. The palette colors are chosen from the remaining nodes. This method is fast and gives good results [6, 9].

As mentioned above, octree color quantization generates the palette using the distribution of colors in the image, but it does not consider the frequency of color. This means that if an image is composed of similar colors overall but has many different low-frequency colors or noise, octree’s results can be very poor.

Figure 1 shows the strengths and weaknesses of octree color quantization. The original image has two high-frequency colors overall and many low-frequency colors around them. Yellowish and bluish colors occupy most of the image. Because SOM quantization considers the distribution of color, the generated palette has 21 yellowish colors and 5 bluish colors. By contrast, the generated palette from octree quantization has 5 yellowish colors and 9 bluish colors. Because of this, it is not influenced by the distribution of colors, and it creates a much greater difference from the original image. This is obviously octree’s weakness.

Figure 1: Strengths and weaknesses of octree color quantization (number of colors ).

However, octree has various colors, like greenish and pinkish, which SOM does not have. This is a strength for octree for the same reason.

In this paper, we present a more effective color quantization algorithm that complements the weakness of SOM by using the strengths of octree, which are fast and offer various colors.

3. SOM Color Quantization

SOM color quantization shows excellent results with most images. The results are very natural and have a low MAE and MSE because the SOM learning algorithm spontaneously considers the distribution of colors.

However, when SOM color quantization has only a small number of colors (i.e., the number of colors, , becomes lower), its disadvantage is apparent. The SOM learning algorithm finds the winner node and updates its weight. At that time, neighbor nodes of the winner are also updated. This means SOM generates a more natural palette, but also it means SOM loses the original color of the neighbor nodes. High-frequency colors are learned many times, so they update the weights many times. This means that high-frequency colors influence neighbor nodes much more, because when the winner is updated, the neighbor nodes are also updated. It becomes more serious when the number of colors, , becomes smaller. If becomes too small, then the SOM map also becomes so small that the influences on updating the winner become much greater. Therefore, the overall colors can become similar and the low-frequency colors disappear through updating the weights.

Figure 2 shows quantized results with 16 colors from SOM [3, 6] and octree [9]. SOM (MAE 28.93, MSE 501.62) obviously has fewer differences from the original image than octree (MAE 56.30, MSE 1609.28). However, visually, the result of octree looks better because the results from SOM lost the sky-blue color, even if octree has almost double the MAE.

Figure 2: The disadvantage of SOM color quantization (number of colors ).

SOM color quantization is excellent method, but the number of colors, , is small; then its performance is decreased. Therefore, it can be a problem when it is used to quantize an image with a small number of colors.

4. Octree-Based SOM Color Quantization

We present a more effective color quantization algorithm by using octree-based SOM color. It complements the disadvantages of SOM, so it not only gives visually natural results but also provides low MAE and MSE.

The beginning of the SOM learning algorithm initializes the weights with random values. It causes similar colors to be located anywhere on the palette. If they are high-frequency colors, then low-frequency colors around them are lost. Therefore, we intentionally initialize the weights with colors in the image. This can induce the results to be more similar to the original image with reduced loss of the original colors. Thus, because the initial weights are fixed, we can always get the same results, unlike traditional SOM.

The weight initialization should satisfy the following conditions to complement the problem above.

(1)The time to create the map must be short.(2)The map should be created using the colors in the image.(3)It should be configured with a variety of colors from the image as much as possible.

Octree color quantization satisfies these conditions. It constructs the octree using the colors in the image, and it prunes them until only nodes remain. It does not need much time to create the palette. In addition, it has various colors because it does not consider the distribution of color. Therefore, the proposed method initializes the weights using the palette generated by octree quantization.

Figure 3 shows the overall process of the proposed method. At first, it builds an octree on the input image and prunes the octree until nodes remain to get the number of colors for initializing the weights of SOM [9].

Figure 3: Overall process of octree-based SOM color quantization.

After initializing, SOM learning is performed. The proposed method uses a two-dimensional SOM and two different learning rates for winner and neighbor nodes. The learning rate for neighbor node is a lot smaller than the learning rate for winner node in order to reduce the loss of low-frequency colors.

The proposed method uses different training data in each iteration. If SOM learns sequential pixels in an image, it means SOM learns similar pixels repeatedly because adjacent pixels in an image have similar colors. The training data for th iteration, , is defined as follows for data sampling:

Determining the winner node and updating the weights are performed by traditional SOM learning method as shown in the following:

Algorithm 1 shows the learning algorithm of octree-based SOM color quantization.

Require: image as a vector of pixels, number of neurons .

Ensure: weight

        Build octree on image and prune the nodes until nodes remain.

         value of pruned nodes

        loop counter

        repeat

                  generate subset of training pixels

                  number of training data

                 adjusted learning rate for winner node

                /100

                for    to    do

                        Determine the winner node

                        Update weight of the winner and its neighbors

                end for

        until weights converged

Algorithm 1: Learning algorithm of octree-based SOM color quantization.

In this paper, it assumes that the weights converge if the average variation in the weights is lower than 0.025, and it is used as an end condition. The end condition is experimental value. Figure 4 shows average MAE, MSE, and processing time of all test cases. When the end condition is set between 0.01 and 0.025, it gives best performances. It shows the end condition 0.025 is reasonable.

Figure 4: Average MAE, MSE, and processing time of all test cases.

5. Experimental Results

The proposed method was tested on a set of ten true-color images commonly used to evaluate performance. The images are shown in Figure 5, and information about the test images is in Table 1. Test images (i) and (j) are collected from Berkeley color image database (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds). All of the color quantization methods were tested on an Intel i5-4460 3.2 GHz, 8.0 GB RAM machine and were implemented in C++.

Table 1: Information on test images.

Image
Resolution
Number of colors
Color difference

(a) Lenna
512 × 512
148,279
97.60

(b) Parrots
768 × 512
72,079
126.36

(c) Mona Lisa
1280 × 1920
125,240
97.14

(d) House
1280 × 1024
363,724
136.87

(e) Mandrill
512 × 512
230,427
118.48

(f) Venus
1848 × 1173
396,799
131.67

(g) Bedroom
1280 × 1024
454,673
136.26

(h) Scream
999 × 1362
189,162
108.71

(i) Old man
321 × 481
33,411
107.84

(j) Dragonfly
481 × 321
41,117
72.24

(a) Lenna

(b) Parrots

(c) Mona Lisa

(d) House

(e) Mandrill

(f) Venus

(g) Bedroom

(h) Scream

(i) Old man

(j) Dragonfly

Figure 5: Test images.

The color differences shown in Table 1 were calculated with (5). As this value increases, the image has various colors, and the differences among the colors are greater:

We evaluated the proposed method by comparing it with well-known color quantization algorithms, such as popularity (POP) [10], octree (OCT) [9], median-cut (MC) [10], -means (KM) [11], Adaptive Resonance Theory 2 (ART2) [12], and Self-Organizing Maps (SOM) [3]. Table 2 explains these color quantization methods.

Table 2: Various color quantization methods [1].

Methods
Features of methods

POP [10]
(i) One of the simplest methods.

(ii) 16 × 16 × 16 color histogram using 4 bits per channel uniform quantization.

(iii) K most-frequent colors in the color histogram are used for quantization.

MC [10]
(i) 32 × 32 × 32 color histogram using 5 bits per channel uniform quantization.

(ii) It makes cubes that include all of the histogram.

(iii) It repeatedly splits the cubes that have the greatest number of colors until K cubes are obtained.

OCT [9]
(i) Tree structure with up to 8 nodes as children, which can represent all colors in an image within an 8-level tree.

(ii) Color distribution is represented using octree, which then prunes the nodes until K nodes remain.

KM [11, 14, 15]
(i) It starts with K random clusters.

(ii) All of the input data are assigned to the cluster that has the minimum distance within the data.

(iii) The centroid of the cluster is calculated as the average of the assigned data.

ART2 [12]
(i) Unsupervised learning model.

(ii) It creates new clusters depending on a vigilance test.

(iii) The palette color is chosen from the centroids of the most-frequent clusters.

SOM [1–5]
(i) Unsupervised learning model.

(ii) One-dimensional self-organizing map with K neurons.

(iii) It designates the minimum distance node as the “winner” node and then updates the weights of the winner node and neighbor nodes.

(iv) It repeats the process until the sum of the weight change is less than a certain threshold.

Tables 3 and 4 show a comparison of MAE and MSE, respectively. means the number of colors used for quantizing an image. The best method is indicated in bold. As expected, the experimental results show that the proposed method gives better results than the conventional SOM method when is small. It is the consequence obtained by reducing the learning rate of the neighbors. It minimizes the influence on the neighbor by the winner, so the proposed method has the original colors of the palette. It means the proposed method is more effective than other algorithms when is small.

Table 3: MAE comparison of the color quantization methods.

Lenna
Parrots

8
16
32
64
128
256
8
16
32
64
128
256

POP
41.5
36.6
22.0
16.1
12.7
11.9
101.6
73.5
58.8
22.5
16.5
13.6

MC
31.0
25.1
19.4
15.1
11.5
8.6
64.8
52.1
36.9
25.1
17.8
12.8

OCT
35.9
32.8
23.3
15.5
12.9
9.6
53.9
38.2
28.6
20.3
16.3
12.6

KM
31.1
30.1
24.8
19.7
13.3
12.0
44.7
31.0
26.6
20.2
16.6
12.9

ART2
31.1
22.1
18.5
12.4
10.3
8.5
43.8
35.3
25.9
18.3
14.2
11.3

SOM
27.4
19.5
14.0
10.9
8.6
6.9
42.4
28.4
20.3
14.9
11.5
8.7

PM
27.1
19.8
14.9
10.9
8.8
7.2
41.0
27.8
20.1
15.2
11.8
9.1

Mona Lisa
House

8
16
32
64
128
256
8
16
32
64
128
256

POP
40.4
28.1
20.4
14.2
12.9
12.5
62.6
52.2
39.1
29.7
21.5
15.8

MC
25.2
20.3
16.1
12.5
9.4
7.4
63.6
54.9
39.6
27.5
20.2
15.9

OCT
29.7
24.8
16.3
13.4
10.4
8.9
51.4
43.3
41.0
25.9
24.8
17.6

KM
30.7
23.5
18.8
14.2
13.6
11.8
43.1
30.8
25.7
22.1
18.5
16.0

ART2
29.2
21.8
16.3
12.3
9.5
7.6
44.1
33.4
29.3
23.6
18.8
15.3

SOM
25.9
17.6
13.3
9.7
7.3
5.6
47.3
33.2
23.7
18.5
14.8
11.8

PM
24.0
16.2
12.0
9.1
7.2
5.7
42.0
30.6
23.8
18.8
14.9
12.0

Mandrill
Venus

8
16
32
64
128
256
8
16
32
64
128
256

POP
78.6
56.9
40.9
31.0
21.1
15.7
109.6
46.5
33.0
23.8
17.3
13.8

MC
69.3
64.6
59.0
46.2
33.8
21.6
41.8
35.7
33.3
27.5
20.3
15.1

OCT
55.3
51.3
39.0
27.2
22.0
17.9
68.6
56.3
34.9
32.0
22.9
17.8

KM
46.3
34.9
30.3
22.5
18.7
16.8
43.5
34.3
25.5
20.8
17.6
14.7

ART2
50.7
37.3
29.2
24.8
20.0
16.3
44.6
37.4
31.2
22.3
17.9
13.7

SOM
45.3
34.0
26.1
20.5
16.4
13.1
42.5
30.3
21.9
16.9
13.4
10.7

PM
45.0
34.1
26.1
20.5
16.8
13.4
40.9
28.4
21.2
16.7
13.4
10.7

Bedroom
Scream

8
16
32
64
128
256
8
16
32
64
128
256

POP
102.1
96.5
56.0
36.1
27.1
19.0
59.3
47.3
29.5
20.9
14.6
12.3

MC
74.0
65.8
47.4
37.8
28.4
19.9
54.5
41.0
28.1
20.5
14.2
10.4

OCT
63.8
57.7
47.9
30.6
26.9
18.1
54.6
53.2
42.1
21.9
17.4
13.0

KM
53.8
40.2
33.0
25.9
21.0
17.3
33.6
27.8
24.2
21.7
15.2
13.0

ART2
52.6
47.5
35.6
27.8
21.7
17.7
33.0
26.3
19.8
15.3
12.5
10.3

SOM
53.8
39.2
28.4
21.8
17.2
13.6
40.5
29.3
18.0
13.6
10.6
8.5

PM
51.7
37.3
28.0
21.7
17.2
13.7
33.1
23.2
17.6
13.7
10.7
8.9

Old man
Dragonfly

8
16
32
64
128
256
8
16
32
64
128
256

POP
71.3
42.6
21.6
13.5
12.9
12.9
39.0
25.8
17.4
14.8
12.8
12.0

MC
27.9
19.0
15.2
11.1
8.8
6.8
26.9
22.7
18.1
13.8
10.9
8.3

OCT
51.6
40.2
17.7
12.0
9.4
7.0
40.2
26.9
22.4
14.3
13.6
9.0

KM
26.8
21.2
18.6
15.8
14.3
12.2
26.5
20.5
20.9
18.3
14.0
13.5

ART2
38.4
27.9
20.9
14.6
11.4
8.5
26.7
23.2
18.1
14.7
11.6
9.3

SOM
22.5
15.9
11.4
8.7
6.5
5.2
21.4
16.2
12.7
10.0
8.9
6.2

PM
22.3
15.7
11.8
8.7
6.8
5.5
21.1
16.9
13.4
10.6
9.6
6.9

Table 4: MSE comparison of the color quantization methods.

Lenna
Parrots

8
16
32
64
128
256
8
16
32
64
128
256

POP
1296.2
1144.6
336.1
175.1
79.8
64.8
8796.1
5976.1
4102.2
362.6
179.5
104.1

MC
577.8
412.4
264.5
163.6
94.8
47.0
2733.2
1716.3
999.7
508.6
274.4
142.9

OCT
692.6
580.6
273.5
126.1
85.4
47.5
1479.2
915.6
469.9
244.6
144.7
85.4

KM
515.3
476.0
334.2
204.9
96.4
77.5
1083.2
534.2
390.6
225.3
150.0
90.7

ART2
517.0
257.9
183.0
82.9
56.0
37.8
1085.8
657.3
353.4
179.7
105.8
68.0

SOM
400.6
210.6
110.0
67.1
42.5
27.8
1027.2
450.2
239.2
133.0
78.8
46.8

PM
396.1
212.1
121.5
66.5
43.1
28.9
960.4
433.1
239.0
135.6
81.0
47.9

Mona Lisa
House

8
16
32
64
128
256
8
16
32
64
128
256

POP
1330.1
658.7
320.2
103.4
76.5
69.0
2758.1
2044.4
1305.1
844.5
399.1
175.6

MC
496.5
330.9
211.7
131.2
60.3
33.2
2407.0
2003.1
1307.8
674.6
338.8
211.0

OCT
536.3
340.3
155.5
97.4
61.0
43.3
1451.2
1058.8
958.5
370.7
339.3
151.9

KM
597.4
339.1
203.0
117.8
103.5
76.4
1040.3
555.7
381.1
276.0
190.2
137.3

ART2
523.1
275.7
147.8
84.0
48.9
30.2
1137.8
620.0
481.0
306.3
189.7
121.6

SOM
483.0
204.1
118.2
60.4
33.7
20.5
1162.8
598.1
326.9
204.2
128.9
81.7

PM
373.2
164.2
89.0
52.1
32.3
20.4
965.6
530.4
322.4
200.9
126.7
81.7

Mandrill
Venus

8
16
32
64
128
256
8
16
32
64
128
256

POP
3878.9
2233.5
1200.6
740.0
305.7
151.7
7759.8
1591.6
899.7
450.8
208.9
110.4

MC
3308.9
3054.1
2614.9
1619.9
801.5
347.2
1107.6
884.4
805.7
577.9
303.0
179.2

OCT
1544.7
1358.6
841.2
379.1
245.6
153.7
2290.2
1609.3
600.3
497.9
286.2
150.6

KM
1137.8
647.2
486.3
266.8
183.4
147.6
1038.6
602.2
342.2
231.4
170.8
120.9

ART2
1339.1
733.4
437.0
319.6
206.3
135.4
987.4
691.0
499.0
261.7
166.0
99.8

SOM
1092.9
627.1
358.3
222.7
144.1
93.1
1067.0
577.3
292.5
173.5
107.3
68.5

PM
1078.2
612.4
354.6
221.8
149.7
94.0
905.9
473.3
263.8
163.6
104.7
67.4

Bedroom
Scream

8
16
32
64
128
256
8
16
32
64
128
256

POP
8107.1
7826.4
2374.7
1061.7
601.6
268.8
2609.4
1859.9
774.7
375.1
128.4
71.8

MC
3429.1
2859.7
1682.0
1121.3
659.6
314.7
1723.1
1149.2
501.3
277.6
133.1
72.8

OCT
2062.4
1753.1
1198.1
480.0
379.3
159.1
1528.1
1474.5
1054.3
256.5
170.2
85.8

KM
1554.1
868.5
588.0
361.6
232.8
156.9
613.1
404.6
312.0
246.7
124.1
88.7

ART2
1465.0
1152.9
647.0
400.9
245.9
158.8
581.6
356.3
204.9
121.3
80.7
54.2

SOM
1591.2
864.1
437.7
258.5
161.0
102.4
936.6
491.3
177.4
102.5
63.0
40.3

PM
1433.4
750.0
423.3
253.0
160.0
101.4
592.1
295.2
166.6
101.8
62.0
42.7

Old man
Dragonfly

8
16
32
64
128
256
8
16
32
64
128
256

POP
3643.5
1551.8
342.2
88.3
78.7
77.9
1427.9
512.6
230.4
157.5
97.4
66.4

MC
522.2
216.7
138.5
80.4
50.6
26.1
449.2
330.3
234.7
161.5
104.7
59.2

OCT
1432.0
764.2
178.3
75.5
46.1
25.4
990.4
386.9
264.2
105.8
95.2
40.8

KM
386.5
245.3
194.0
138.6
115.9
79.6
387.1
235.7
239.7
183.8
108.4
99.2

ART2
715.5
388.4
245.1
115.6
69.9
38.0
390.7
287.4
175.1
113.3
69.7
44.5

SOM
270.7
138.5
75.4
41.1
24.9
15.8
271.4
161.2
98.9
60.8
43.8
24.3

PM
266.9
135.4
78.5
41.2
25.1
16.3
264.3
171.6
103.1
63.2
50.1
26.6

On the other hand, the disadvantage of SOM is reduced when SOM uses a big enough size of map for learning, because the influence of the winner is decreased. Therefore, the performance gap between conventional SOM and the proposed method also decreases when is large. In some cases, the proposed method gives a larger MAE and MSE, because the low learning rate of the neighbor disturbs similar color grouping on the map.

Table 5 shows the processing time of the color quantization methods. The proposed method is faster than the conventional SOM method because the weights are initialized with the palette generated by octree. On the other hand, the conventional SOM method initializes the weights with random values. It makes SOM need less time before the weights converge. Therefore, the proposed method requires only 71.73% of the processing time of the conventional SOM method.

Table 5: Processing time (ms) comparison of the color quantization methods.

Lenna
Parrots

8
16
32
64
128
256
8
16
32
64
128
256

POP
2
2
2
2
3
3
3
3
3
4
4
6

MC
3
2
2
2
2
3
3
3
4
3
4
4

OCT
6
6
7
7
8
8
9
11
10
12
12
13

KM
46
63
97
87
170
323
65
84
124
207
527
665

ART2
31
46
107
88
300
621
68
102
190
360
520
1001

SOM
93
107
139
213
333
548
148
160
207
311
471
825

PM
73
79
97
147
302
387
116
131
162
214
330
581

Mona Lisa
House

8
16
32
64
128
256
8
16
32
64
128
256

POP
21
19
28
23
21
23
11
11
11
11
12
16

MC
23
21
20
21
21
23
12
12
12
12
11
12

OCT
56
58
65
66
74
75
30
29
32
33
35
44

KM
321
646
778
1179
2246
2074
268
337
540
753
1150
2192

ART2
300
457
524
765
1462
6121
406
676
896
1930
3833
7722

SOM
863
938
1347
1678
2848
4621
485
681
754
1157
1791
2814

PM
612
708
896
1260
1979
3213
359
514
530
721
1135
1904

Mandrill
Venus

8
16
32
64
128
256
8
16
32
64
128
256

POP
2
2
3
3
3
5
18
18
20
20
19
22

MC
2
3
3
3
3
3
18
18
20
20
18
20

OCT
5
6
6
7
7
8
50
49
57
58
58
58

KM
46
66
96
134
230
441
455
487
740
1133
2065
3458

ART2
62
91
201
408
696
1428
631
760
1503
3408
6930
15597

SOM
108
130
156
220
467
593
953
985
1286
1839
3239
4898

PM
73
83
104
153
332
395
623
755
904
1246
1920
3325

Bedroom
Scream

8
16
32
64
128
256
8
16
32
64
128
256

POP
12
12
20
11
11
14
10
11
11
20
14
13

MC
10
13
12
11
13
13
13
18
13
13
12
12

OCT
28
28
30
31
33
66
32
31
34
38
37
42

KM
231
295
431
685
1159
2346
261
341
440
701
1183
1975

ART2
338
653
989
2183
4517
9675
179
210
624
1086
2026
4287

SOM
520
825
979
1103
1779
2944
477
688
740
1169
1700
3024

PM
349
549
515
726
1139
2063
413
633
525
841
1214
2054

Old man
Dragonfly

8
16
32
64
128
256
8
16
32
64
128
256

POP
1
1
1
1
2
2
3
1
1
2
2
2

MC
1
1
1
1
1
1
1
1
1
1
1
2

OCT
3
3
4
4
5
5
3
4
4
4
4
5

KM
19
26
41
39
56
72
19
27
44
68
119
163

ART2
14
16
19
29
43
76
16
19
27
40
72
259

SOM
37
46
78
100
155
257
37
47
64
100
162
277

PM
33
37
52
73
109
195
30
34
47
69
136
194

In Figure 6, the peak signal-to-noise ratio (PSNR) is additionally measured to evaluate quantization quality. PSNR is a popular measure to evaluate the reconstruction quality of image compression codecs and thus is used to evaluate color compression quality. A higher PSNR means the color quantization method has higher quality [13]. PSNR is defined as shown in the following:

Figure 6: Average MAE, MSE, PSNR, and processing time of each method.

Figure 7 shows that the proposed method gives more similar results to the original image, visually, even if it has similar MAE and MSE. Overall colors in the palette generated by SOM become similar if is small. However, the proposed method has colors more similar to the original image because of the low learning rate of neighbors and initialization by octree colors. The proposed method gives more similar sky color on scream image and gives more similar background color on Mona Lisa image.

Figure 7: Resulting images from the color quantization methods ().

6. Conclusions

In this paper, an effective color quantization method is proposed. Having many colors in the original image is a serious problem for image processing, so the number of colors must be reduced. Therefore, we propose an octree-based SOM color quantization method. It is particularly more effective than other algorithms when the number of colors, , is small.

It uses an octree color quantization method to complement the disadvantages of SOM color quantization, which is one of the more effective methods. The octree color quantization method does not consider the distribution of colors, so its palette includes various colors. Therefore, we use the palette generated by octree to initialize the SOM weights. This causes the palette generated by SOM to have more varied colors than conventional SOM. In addition, the learning rate of the neighbors is set to low to minimize the loss of color information from updating the winner, because the influence of the winner becomes greater when is small. The low learning rate of the neighbor means the proposed method can have colors similar to the original image.

The proposed method was evaluated by comparing it with six well-known quantization methods. MAE, MSE, and processing time are measured for comparison. The experimental results show that the proposed method is more effective than other methods when it uses a smaller number of colors to quantize the colors in the image (). It gives lower MAE and MSE. Thus, the proposed method requires only 71.73% of the processing time of the conventional SOM method. On the other hand, when it uses a large number of colors, the performance of the proposed method gives similar MAE and MSE with conventional SOM, but it is still faster.

It is expected that the proposed method will be available to many applications that have a problem with varied colors in images.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This work was supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence.

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