LOGO Image Compression: Mathematical Means The Importance of
Image Compression: Mathematical Means
The Importance of Compression at NASA
Image/video data compression is a very critical technology for numerous operations at NASA. At
NASA, image compression is done for three main reasons. First, compression saves space. NASA
receives millions of information/data each day that requires a huge storage facility, so
compression saves enormous amounts of space on the hard drive. Secondly, image/video
compression saves transmission time. Due to the massive distance between Mars and Earth, for
example, sending back pictures and data for the NASA Mars Rover project can take years if the
data is uncompressed. Lastly, compression saves money simply by saving hard drive space and
NASA identified various lunar/Mars mission requirements that involve transmission of
image/video. These can be categorized into several types: high rate video, edited high rate
video, low rate video, science imaging data, and telerobotic video. Some of this image/video
data can benefit greatly from compression because it would take up less space and can
therefore be transmitted faster. Other image/video data such as scientific/medical data and
telerobotics videos are very valuable and irreplaceable, so NASA is reluctant to consider any
type of compression on these.1
A study of the basic fundamentals of image compression was conducted in an effort to inform
NASA about the most efficient means of compression. NASA uses images to reveal information,
data, and evidence concerning astronomical research. For this reason each NASA image must
have the best quality and adequate dimensions. The purpose of this project was to compare the
compression ratios resulting from compression by three variations of pixel matrices, and to
understand the difference between arithmetic mean and geometric mean compression
methods. For this task, a total of 50 planetary images were selected from the NASA website.
With the aid of the Mathematica software, the team created a program to compress the images.
The team then recorded its findings from the experiment in the form of graphs; visual
representation helped to understand the resulting trends.
1 + 2+...+
Formula for Geometric Mean:
= 1 2 ...
To find the most efficient means of compression, the team compared two different compression
mechanisms: arithmetic and geometric mean compression. Within each broad compression
mechanism, we looked at images from five different categories Venus, Earth, Mars, Jupiter,
and Saturn (all images were taken from the NASA website). We compressed each photo in each
category in three different ways: by a 1x4 matrix, 2x2 matrix, and a 2x3 matrix.
To carry out the experiment, we used a
computational software program called
Mathematica. The image was first separated
into three distinct color channels: red, green,
After compressing all of the
images, the team compared the
compression ratios. A
compression ratio is defined by
the original file size divided by
the file size of the compressed
Each separate image is then converted into
a matrix; that is the only way the computer
can understand and manipulate the image.
We then set a variation (i.e., 2x2, 2x3, 1x4
matrix). Infinite zeros are then added to the
right and below the image to account for
compression by a matrix which does not
divide the dimensions of the original image.
Following this, each of the three images (R,
G, B color channels) is compressed by the
desired variation. The compressed images
are the combined, yielding a compressed
version of the original image.
The teams hypothesis is tri-fold:
1. The geometric mean compression will save less space than arithmetic mean compression.
2. An image that is compressed by a 2x3 matrix saves more space than one that is compressed
by fewer pixels (i.e., a 1x4 and 2x2 matrix). An image compressed by a 2x2 matrix and 1x4
matrix will save the same amount of space. In other words, the orientation of the matrix does
not matter as long as it has the same number of pixels.
3. The compression ratio is not affected by the different categories (Venus, Earth, Mars, Jupiter,
Conclusion and Future Work
The original image files with the lowest and highest file sizes render a lower compression
ratio while the middle range file sizes has a higher compression ratio. This is the reason why
Mars has a larger compression ratio.
Based on the arithmetic mean sampling technique, the larger the matrix size, the redder the
Based on the geometric mean sampling technique, the larger the matrix size, the bluer the
The geometric and arithmetic mean methods indicate that the higher the sample matrix
size, the greater the compression ratio.
Compression by the same number of pixels (for example 2x2 and 1x4) saves roughly the
same amount of space (for both the arithmetic and geometric means).
In the future, the team hopes to hopes to study other algorithms that will explain the change of
color associated with the arithmetic and geometric mean compressions. Why does compression
by the arithmetic mean produce red images while compression by the geometric mean produce
Sponsors and Contributors
National Aeronautics and Space Administration
NASA Goddard Space Flight Center (GSFC)
NASA Goddard Institute for Space Studies
CUNY Hostos CC & City Tech
Prof. Tanvir Prince (faculty mentor)
With special thanks to Prof. Nieves Angulo.
Variations (2x2, 1x4, 2x3) vs. Compression Ratio:
How does changing the matrix size affect the compression ratio for both the arithmetic and
Karina Shah (high school student)
William Ashong (undergraduate student)
Ildefonso Salva (high school teacher)
RESEARCH POSTER PRESENTATION DESIGN 2012
As technology is rapidly advancing, computerized images must maintain their quality, even
after compression. Compression is really just a mathematical algorithm that is applied to
an images content, and as such, altering these algorithms could significantly improve
On the other hand, lossy compression reduces a file by permanently eliminating certain
information, especially redundant information. After the file is compressed, only a part of
the original information remains, although the user may not notice it. Lossy compression is
generally used for video and sound, where a certain amount of information loss will not be
detected by most users and is of no consequence.
Average Compression Ratio
Image compression is the reduction of image pixels (bytes) in order to minimize the
memory an image saves on a computers hard drive. Image compression has a wide
variety of multi-media application: digital cameras, computers, smartphones, in addition to
several peripheral devices. Images can be compressed in two distinct ways by lossless
and lossy compression. In a lossless compression process, no data is lost in the
compression. This technique is generally used for text or spreadsheet files, where losing
words or financial data often poses a problem.
Average Compression Ratio
Arithmetic vs. Geometric Mean:
Does compression by arithmetic mean save more space than compression by geometric
mean, or vice versa? Is there any difference at all between the amount of space each
compression mechanism saves?
Average Compression Ratio
Formula for Arithmetic Mean:
Average Comression Ratio
Arithmetic and Geometric Mean Formulas
Stefany Franco, Ildefonso Salva, Charlie Windolf, Tanvir Prince, Mathematics Behind Image Compression, (New York: Journal of Student Research), p. 1.
Applying the principles of matrices to examine the difference between compression by
arithmetic mean and compression by geometric mean, specifically understanding which
method saves more space.
Using hands-on experience on the Mathematica software to determine how changing matrix
size affects the compression ratio for both geometric and arithmetic mean compression
Average Compression Ratio
Methods and Materials
Color Changes for Arithmetic and Geometric Mean Compression
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