=Paper= {{Paper |id=Vol-1730/p02 |storemode=property |title=First Attempt of Rapid Compression of 2D Images Based on Histograms Analysis |pdfUrl=https://ceur-ws.org/Vol-1730/p02.pdf |volume=Vol-1730 |authors=Danuta Jama |dblpUrl=https://dblp.org/rec/conf/system/Jama16 }} ==First Attempt of Rapid Compression of 2D Images Based on Histograms Analysis== https://ceur-ws.org/Vol-1730/p02.pdf

First Attempt of Rapid Compression of 2D Images
Based on Histograms Analysis
Danuta Jama
Institute of Mathematics
Silesian University of Technology
Kaszubska 23, 44-100 Gliwice, Poland
Email: Danuta.Jama@polsl.pl

Abstract—Expanding technological advances and digitization size of digital data is just one of the reasons for developing this
generate more and more data, so the amount of space to subject. The second reason is the considerable development
store them is greater. To minimize the amount of stored data, of related branches of computer science such as artificial
efficient compression algorithms are needed. In this paper, the
idea of the use of histograms for analysis of 2D images for intelligence methods that expand the possibilities of action of
the purpose of compression is shown. Performance tests were compression algorithms.
carried out, presented and discussed in terms of advantages and In 2012, Vikas Goyal [1] introduced the use of wavelet
disadvantages. coding for the purpose of minimizing the size of image files.
In addition, analysis of the use of different wavelets (including
I. I NTRODUCTION
Haar or Dauchechies) EZW algorithm has been presented.
Digital imagery has a huge impact on our lives. In every Similarly, the authors of [2] presented an algorithm based on
place on earth, at any time, a person has access to their data the combination of wavelet theory and chaos theory (fractals).
using a laptop or a cell phone connected to the Internet. And For comparison, Cenugopal et al. [3] proposed the use of
thus, the exchange of data between two points on the globe is Arnold transform with chaos encoding technique showing the
no longer a problem. On the other hand, Internet has caused effectiveness of the creation of hybrid algorithms. In a similar
a wave of popularity of various social networking sites where time, Zhou et al. [4] presented the idea of encryption hybrid
people publish various photos of their life. This is just one algorithm based on key-controlled measurement matrix.
of the phenomena of the last years, which intensified the The rapid development of artificial intelligence methods
amount of data and exchange information in the network at allowed to take a more random direction of compression,
least several times. which can be seen on the example of the use of heuristic
These issues have caused several problems. First, the data algorithms [5]–[7], or fuzzy logic [8], [9], what helped gain a
traffic burden the entire network, so the upload speed or significant advantage over other existing algorithms in terms of
download can be reduced at the time. Secondly, the amount weight input files. And in [10], the authors introduced a novel
of data grows every day - files must be stored somewhere, so predictor using causal block matching and 3D collaborative
the number of servers will also increase. These are problems filtering.
that can not be solved, but we can minimize them. Increasingly, modern computer science uses the latest
Data compression algorithms rely on conversion method of achievements, but that does not mean that the older mathe-
writing data in such a way that the weight of the data was matical theories such as Fourier transforms are no longer used
smaller. In other words, the input file should be processed in – in [11]–[14], a new approach to the use of either transform
order to save the file to a smaller number of bits. is described. Again in [15], Fracastoro et al. pointed to the use
Compression algorithms can be divided into two types - of transformation based on graphs.
lossless and lossy. Lossless methods do not change the con- In this paper, lossy compression algorithm for 2D images
tents of the file, so the only form of writing a file is changed. based on the analysis of the histogram is presented.
The best-known algorithms is Huffman and Shannon-Fano
coding, LZW or PNG. On the other hand, lossy algorithms III. C OMPRESSION A LGORITHM
operate by manipulating not only a record but also the quality To optimize the amount of compressed material from two-
of the file, eg .: DPCM or JPEG. dimensional images, specific areas will be analyzed using the
mechanism of the grid. The grid of size n pixels gets the area
II. R ELATED W ORK of the image and processes it. Then, the grid is shifted by n
In recent years, data compression algorithms are experienc- pixels and the operation is repeated until the entire image will
ing a renaissance. The demand for methods of reducing the not be covered with the grid.
The processing of the the grid is to calculate the histogram
Copyright c 2016 held by the author. and check whether the histogram exceeds the threshold value
γ (see Fig. 2, where a sample histogram is cut by the threshold

9
value). In the case where histogram of the area covered by a
grid exceeds the threshold value, all the colors of pixels are
scaled using the following formula
(
αK if αK < 256
CK = , (1)
255 if αK > 255
where K means a specific color component from the RGB
model (R, G or B) and α is a given parameter in the range of
h0, 2i.
The parameter α manipulates the brightness of the image
what can be represented as

h0, 1)
 dim image
α= 1 normal image . (2)

(1, 2i brighter image


The implementation of the described technique is presented
in Algorithm 1.
Algorithm 1 The algorithm color change based on a histogram
1: Start
2: Load the image
3: Define the grid sizen and a threshold value γ
4: Define an array representing the values of the histogram
5: max := 0
6: while grids does not cover the entire image do
7: Load the grid on the current position
8: for each pixel in the grid do
9: Get the red value of a pixel to actualV alue
10: Increase the value of 1 in the array histogram at the
position actualV alue
11: if max < actualV alue then
12: max = actualV alue
13: end if
14: end for
15: θ := f alse
16: for each value ω in the histogram array do
17: if ω ≥ γ then
18: θ = true
19: end if
20: end for
21: if θ == true then
22: for each pixel in the grid do
23: Recalculate color value using (1)
24: end for
25: end if
26: Move the position of the value of n
27: end while
28: Stop

IV. E XPERIMENTS
The algorithm has been tested for different sized images
from the database Earth Science World Image Bank (available
Fig. 1: A simplified model of the algorithm. here – www.earthscienceworld.org). During the tests, the grid
The images were used only for research purposes and the author does not
derive any financial benefit.

10
Fig. 2: The histogram with selected threshold value (red line).

size was set to 81 pixels, and thus 9 × 9 and a threshold value Fig. 4: The dependence of the average time from the size of
was set as 0.4. the image.
Performed tests showed that for the small parameters α
and γ, the image is darker but with a smaller size. The
measurement results can be seen in Figures 5, 6 and 7. The In future work, the design of the algorithm reverse is
weight of each file was measured before and after compres- planned. This method, lossy compression of the file and the
sion. Graphical comparison of the size is shown in Figure 3. ability to recover lost data would enable a much better flow
The effectiveness of the proposed method was measured using of data on the Internet, because the two sides could have the
the arithmetic mean. For some parameters, the compressed original file, where the transferred file would be not only lower
image has average 3.1 times lower weight. The dependence quality, but much smaller size.
of the average running time of the algorithm from the size R EFERENCES
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(a) 479KB (b) 173KB

12
(a) 188KB (b) 61,6KB

(e) 87,4KB (f) 26,3KB
Fig. 6: Examples of quality compression.

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(a) 186KB (b) 63KB
Fig. 7: Example of quality compression.

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