Image Processing in Android Environment AJCSE

Corresponding Author: Karan Bansal, Email: karanbansal95@yahoo.co.in
RESEARCH ARTICLE
www.ajcse.info
Asian Journal of Computer Science Engineering2016; 2(1):11-14
Image Processing in Android Environment
1
Karan Bansal*, 2
Aman Bansal
1, 2*
IT Student Maharaja Agrasen Institute of Technology, (GGSIPU), New Delhi
Received on: 25/10/2016, Revised on: 20/11/2016, Accepted on: 14/12/2016
ABSTRACT
Image Processing is processing of images using mathematical operations by using any form of signal
processing for which the input is an image and the output is any processed image having some operations
applied on it. In Android environment we have compressed image, saturated image and cropped image
with different mathematical operations and various techniques such as J-peg Lossy Compression
technique. Adding text to image and decreasing-increasing brightness and contrast of image also come
under processing of images.
Keyword: Android Processing, Image Processing, Image Processing System
INTRODUCTION
Image processing is processing of images using
mathematical operations by using any form
of signal processing for which the input is an
image, a series of images, or a video, such as
a photograph or video frame; the output of image
processing may be either an image or a set of
characteristics or parameters related to the
image.[1]
Most image-processing techniques
involve treating the image as a two-
dimensional signal and applying standard signal-
processing techniques to it. Images are also
processed as three-dimensional signals with the
third-dimension being time or the z-axis.
Closely related to image processing is computer
graphics and computer vision. In computer
graphics, images are manually made from
physical models of objects, environments, and
lighting, instead of being acquired (via imaging
devices such as cameras) from natural scenes, as
in most animated movies. Computer vision, on the
other hand, is often considered high-level image
processing out of which
machine/computer/software intends to decipher
the physical contents of an image or a sequence of
images (e.g., videos or 3D full-body magnetic
resonance scans).
In modern sciences and technologies, images also
gain much broader scopes due to the ever growing
importance of scientific visualization (of often
large-scale complex scientific/experimental data).
Examples include microarray data in genetic
research, or real-time multi-asset portfolio trading
in finance. Image Compression is one of
important feature of Image processing. Image
Compression is a type of data compression
applied to digital images, to reduce their cost for
storage or transmission. Algorithms may take
advantage of visual perception and statistical
properties of image data to provide superior
results compared to generic compression
methods.[2]
The best image quality at a given bit-rate (or
compression rate) is the main goal of image
compression, however, there are other important
properties of image compression schemes:
Scalability: Scalability generally refers to a
quality reduction achieved by manipulation of the
bitstream or file (without decompression and re-
compression). Other names for scalability
are progressive coding or embedded bit streams.
Despite its contrary nature, scalability also may be
found in lossless codec’s, usually in form of
coarse-to-fine pixel scans. Scalability is especially
useful for previewing images while downloading
them (e.g., in a web browser) or for providing
variable quality access to e.g., databases. There
are several types of scalability:

Bnasal Karan et al. Image Processing in Android Environment
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© 2015, AJCSE. All Rights Reserved.
• Quality progressive or layer progressive:
The bit stream successively refines the
reconstructed image.
• Resolution progressive: First encode a
lower image resolution; then encode the
difference to higher resolutions.[3][4]
• Component progressive: First encode
grey; then color.
• Region of interest coding- Certain parts
of the image are encoded with higher
quality than others. This may be combined
with scalability (encode these parts first,
others later).
Meta information- Compressed data may contain
information about the image which may be used
to categorize, search, or browse images. Such
information may include color and texture
statistics, small preview images, and author or
copyright information.
Processing power- Compression algorithms
require different amounts of processing power to
encode and decode. Some high compression
algorithms require high processing power.
COMPRESSION TECHNIQUES AND USES
Lossless Image compression is used in many
applications. For example, it is used in the ZIP file
format and in the GNU tool g-zip. It is also often
used as a component within lossy data
compression technologies (e.g. lossless mid/side
joint stereo pre-processing by
the LAME MP3 encoder and other lossy audio
encoders).
Lossless compression is used in cases where it is
important that the original and the decompressed
data be identical, or where deviations from the
original data could be deleterious. Typical
examples are executable programs, text
documents, and source code. Some image file
formats, like PNG or GIF, use only lossless
compression, while others
like TIFF and MNG may use either lossless or
lossy methods. Lossless audio formats are most
often used for archiving or production purposes,
while smaller lossy audio files are typically used
on portable players and in other cases where
storage space is limited or exact replication of the
audio is unnecessary.
Lossy Image compression is most commonly
used to compress multimedia data (audio, video,
and images), especially in applications such
as streaming media and internet telephony. By
contrast, lossless compression is typically required
for text and data files, such as bank records and
text articles. In many cases it is advantageous to
make a master lossless file which is to be used to
produce new compressed files; for example, a
multi-megabyte file can be used at full size to
produce a full-page advertisement in a glossy
magazine, and a 10 kilobyte lossy copy can be
made for a small image on a web page.
JPEG LOSSY COMPRESSION: The term
"JPEG" is an initialism/acronym for the Joint
Photographic Experts Group, which created the
standard. The MIME media type for JPEG
is image/jpeg, except in older Internet
Explorer versions, which provides a MIME type
of image/jpeg when uploading JPEG
images.[5]
JPEG files usually have a filename
extension of .jpg or .jpeg.
JPEG compression is used in a number of image
file formats. JPEG/gif is the most common image
format used by digital cameras and other
photographic image capture devices; along with
JPEG/JFIF, it is the most common format for
storing and transmitting photographic images on
the World Wide Web.[6]
These format variations
are often not distinguished, and are simply called
JPEG.
J-PEG COMPRESSION OVER OTHER
COMPRESSION
The advantage of lossy methods
over lossless methods is that in some cases a lossy
method can produce a much smaller compressed
file than any lossless method, while still meeting
the requirements of the application. Lossy
methods are most often used for compressing
sound, images or videos. This is because these
types of data are intended for human interpretation
where the mind can easily "fill in the blanks" or
see past very minor errors or inconsistencies –
ideally lossy compression
is transparent (imperceptible), which can be
verified via an ABX test. Data files using lossy
compression are smaller in size and thus cost less
to store and to transmit over the Internet, a crucial
consideration for streaming video services such
as Netflix and streaming audio services such
as Spotify.
Transparency
When a user acquires a lossily compressed file,
(for example, to reduce download time) the
retrieved file can be quite different from the
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original at the bit level while being
indistinguishable to the human ear or eye for most
practical purposes. Many compression methods
focus on the idiosyncrasies of human physiology,
taking into account, for instance, that the human
eye can see only certain wavelengths of light.
The psychoacoustic model describes how sound
can be highly compressed without degrading
perceived quality. Flaws caused by lossy
compression that are noticeable to the human eye
or ear are known as compression artifacts.
Compression ratio
The compression ratio (that is, the size of the
compressed file compared to that of the
uncompressed file) of lossy video codecs is nearly
always far superior to that of the audio and still-
image equivalents.
• Video can be compressed immensely (e.g.
100:1) with little visible quality loss
• Audio can often be compressed at 10:1
with imperceptible loss of quality
• Still images are often lossily compressed
at 10:1, as with audio, but the quality loss
is more noticeable, especially on closer
inspection.
PERFORMANCE COMPARISON
Image processing must be done by using precise
mathematical operations which can provide
optimal results. Image saturation is also important
part of Image processing. With the help of RBGA
values we change image to greyscale or in
different color according to design by adjusting
value from(0 to 255).In Android platform, CPU
usage, GPU(Graphical Processing Unit) and
RAM(Random Access Memory) provide certain
criteria on which application can be compared
with other applications. To study about different
criteria and comparing with other features
Android Monitor is used which produces results
such as-
MEMORY Usage:-
Figure-1: Image processing Memory
ColorMatrix matrix = new ColorMatrix();
matrix.setSaturation(0);
ColorMatrixColorFilter filter = new
ColorMatrixColorFilter(matrix);
imageview.setColorFilter(filter);
Figure-2: Adding Saturation in Image processing
CPU Usage:-
Figure-3: Image processing CPU Usage
By using an optimal GUI and optimal
mathematical operations and decreasing load on
application we can have low Graphical usage
which leads to less memory consumption and less
CPU usage so Devices that can do not have high
processing power or with limitations in CPU one
can use image processing features such as:
Figure-4: Other Image processing GPU value
Figure-5: Optimized Image Processing GPU value.
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CONCLUSION
The purpose of image processing is divided into 4
groups. They are:
• Visualization - Observe the objects that are
not visible.
• Image sharpening and restoration - To
create a better image.
• Image retrieval - Seek for the image of
interest.
• Measurement of pattern – Measures
various objects in an image.
It can be viewed as necessity of using
mathematical operations on pixels and using
optimized functions which consume less memory,
CPU usage, GPU usage in image processing.Use
of compressing technique must also be taken care
of whether using lossy or lossless compression
technology as both has their advantages and
disadvantages over each other. We have JPEG
lossy technology in our application due to its high
performance, Region of Interest coding,
Scalability than lossless compression technology
and other lossy technology. For saving the quality
of image, we have initially converted image in
Bitmap format the applied lossy compression
technique with 90% size compression and then we
have converted bitmap to image. For adding
saturation in Image processing we have converted
image to bitmap values of R,B,G (Red, Blue,
Green) and by adjusting its value color of image
can be changed or color of text on image can be
changed ,in matrix.setSaturation() function values
varies from(0 to 255) .
REFERENCES
1. Rafael C. Gonzalez; Richard E. Woods
(2008). Digital Image Processing. Prentice
Hall. pp. 1–3. ISBN 978-0-13-168728-8.
2. DataCompression:http://user.engineering.u
iowa.edu/~dip/lecture/DataCompression.ht
ml
3. Burt, P.; Adelson, E. (1 April 1983). "The
Laplacian Pyramid as a Compact Image
Code". IEEE Transactions on
Communications. 31 (4): 532–
540. doi:10.1109/TCOM.1983.1095851.
4. Shao, Dan; Kropatsch, Walter G.
(February 3–5, 2010). "Irregular Laplacian
Graph Pyramid" (PDF). Computer Vision
Winter Workshop 2010. Nove Hrady,
Czech Republic: Czech Pattern
Recognition Society.
5. Haines, Richard F.; Chuang, Sherry L. (1
July 1992). The effects of video
compression on acceptability of images for
monitoring life sciences
experiments (Technical report). NASA.
NASA-TP-3239, A-92040, NAS
1.60:3239. The JPEG still-image-
compression levels, even with the large
range of 5:1 to 120:1 in this study, yielded
equally high levels of acceptability
6. "HTTP Archive - Interesting
Stats". httparchive.org.
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Image Processing in Android Environment AJCSE

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