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Image Processing Algorithm JPEG to Binary Conversion

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					                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                             Vol. 8, No. 2, May 2010




                    Image Processing Algorithm
                                    JPEG to Binary Conversion

         Mansi Gupta                              Meha Garg                                 Prateek Dhawan
   Dept. of Computer Sc. &                   Dept. of Computer Sc. &                     Dept. of Computer Sc. &
            Engg.,                                    Engg.,                                      Engg.,
    Lingaya’s University,                     Lingaya’s University,                       Lingaya’s University,
   Faridabad, Haryana,India                  Faridabad, Haryana,India                    Faridabad, Haryana,India
  manasigupta18@gmail.com,                    mehagarg.be@gmail.com                       prateek.3212@gmail.com


Abstract – The JPEG processing algorithm works best            As for the LAB colour space, L* stands for
on photographs and paintings of realistic scenes with          luminance, a* is the red-green axis, and b* is the
smooth variations of tone and colour but is not well           blue-yellow axis. The asterisks were added to
suited to files that will undergo multiple edits. The
                                                               differentiate CIE from another L,a,b model.[3]
direct conversion of jpeg image into binary format is
                                                               Although CIE L*a*b* has a large color gamut and
very low in efficiency. In this paper, the process of
conversion of jpeg image to binary image is being
                                                               is considered as the most accurate colour model, it
done in a step by step manner, without using direct            is often used as a reference only or as an
inbuilt function of jpeg to binary in MATLAB. As the           intermediary for colour space conversion.
binary image is used for comparison purposes, the
jpeg image is converted into LAB format to make the                 II.       VARIOUS   METHODS     FOR
luminance scale perceptually more uniform, so that                            COMPUTING BINARY IMAGE
the procedure becomes more efficient.
                                                               The JPEG image can be converted into Binary
Keywords: LAB, Binary image, sign language
                                                               image by writing codes using C# or Visual Basic.
    I.       INTRODUCTION
                                                               This conversion can also be implemented by
JPEG (named after the Joint Photographic Experts               conversion of RGB into grayscale first and then
Group who created the standard) is a commonly                  into binary.
used method of lossy compression for photographic
                                                               It can also be done with the help of an inbuilt
images. [1]
                                                               function in MATLAB. The function is
                                                               im2bw(RGB, level). Applying this function on the
Another format is the binary format which has
                                                               image for alphabet A generates a corresponding
pixels with only two possible intensity values.
                                                               binary image in fig.1
They are normally displayed as black and white.
Numerically, the two values are often 0 for black,
and either 1 or 255 for white.

 Binary images are often produced by thresholding
a grayscale or color image, in order to separate an
object in the image from the background. The color
of the object (usually white) is referred to as the                   Fig.1 JPEG and Binary image for alphabet A
foreground color. The rest (usually black) is
referred to as the background color. However,                       III.      PROBLEM DEFINITION
depending on the image which is to be threshold,
this polarity might be inverted, in which case the             The object is hands of the sign language useer
object is displayed with 0 and the background is               which must be in fully in black when displayed in
with a non-zero value. [2]                                     the binary image. The image in fig. 1 is not clear
                                                               and has distortions too, i.e., the hand portion is not
                                                               in black completely. This would hinder gaining




                                                        75                              http://sites.google.com/site/ijcsis/
                                                                                        ISSN 1947-5500
                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                  Vol. 8, No. 2, May 2010




higher efficiencies in further processing of the                    colour axis L*, a* and b*. The image is then
image, if required.                                                 viewed in these colour spaces and the sensitivity is
                                                                    tested. After testing the sensitivity, a suitable
With most gestures one-handed, signs maybe one-                     threshold value of L*, a*, b* or an appropriate
handed (ASL) or two handed (BSL).                                   combination of either a* and b* or any other colour
                                                                    axis is taken for formulating the binary images.
Using colours to identify users’ hands may pose
                                                                    There are different set of values for detecting the
problems when there are uncontrolled backgrounds
                                                                    skin and differentiating it with the background
[5] depicted in fig.5
                                                                    colour.




           Fig.5 Image for alphabet A

Few signs are often very similar (or even identical)                       Fig.8 LAB Image for alphabet A
in there manual features but differ in non-manual
                                                                    After the implementation of the specified values,
features (Fig. 6)
                                                                    the image can finally be converted into a binary
                                                                    form




                                                                           Fig.9 Binary Image for alphabet A

                                                                    All those pixels that have their values in this
           Fig. 6 Images for L, M, N and V alphabets
                                                                    specified range are given a value of 0, i.e. white
                                                                    and rest all the other pixels are given a value of 1,
    IV.       METHODOLOGY
                                                                    i.e. black.
First the JPEG image is filtered to reduce noise and
enhance the visual quality of the input image.                           V.        APPLICATIONS
Filtering constitutes an important part of any image
                                                                    The binary images can be generated for all the
processing pipeline where the final image is
                                                                    alphabets of BSL sign language and can be used for
utilized for visual inspection or for automatic
                                                                    recognizing the alphabets. This would eliminate the
analysis. [4] This preprocessing helps increase the
                                                                    need for sensors and other devices like digital
performance       of    the    subsequent     stages.
                                                                    gloves which have been used in sign recognition
                                                                    previously.
                                                                    Also, it would greatly increase the efficiency for
                                                                    further image processing, if required, because of
                                                                    the near-perfect and low noise images produced.



                                                                         VI.       ADVANTAGES OF BINARY
      Fig.7 Filtered Image for alphabet A
                                                                    •     Easy to acquire: simple digital cameras can
Then the filtered image in RGB colour space is                            be used together with very simple frame
converted into LAB colour space. In LAB format,                           stores, or low-cost scanners, or thresholding
the figure can be segmented into three different                          may be applied to grey-level images.



                                                             76                               http://sites.google.com/site/ijcsis/
                                                                                              ISSN 1947-5500
                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                 Vol. 8, No. 2, May 2010




•    Low storage: no more than 1 bit/pixel, often                                      REFERENCES
     this can be reduced as such images are very
                                                                   [1] http://en.wikipedia.org/wiki/JPEG
     amenable to compression (e.g. run-length
     coding).                                                      [2]http://www.codersource.net/csharp_color_image_to_b
                                                                         inary.aspx
•    Simple processing: the algorithms are in most
     cases much simpler than those applied to                      [3]http://www.answers.com/topic/cie-lab
     grey-level images.
                                                                   [4]http://encyclopedia.jrank.org/articles/pages/6691
                                                                         /Color-Image-Filtering-and-Enhancement. html

    VII.       DISADVANTAGES OF BINARY                             [5] A multimodal framework for the communication of
               IMAGES                                                   the disabled Savvas Argyropoulos 1, Konstantinos
                                                                        Moustakas 1, Alexey A. Karpov 2, Oya Aran 3,
                                                                        Dimitrios Tzovaras 1, Thanos Tsakiris 1, Giovanna
    •      Limited application: as the representation                   Varni 4, Byungjun Kwon 5
           is only a silhouette, application is
           restricted to tasks where internal detail is
           not required as a distinguishing
           characteristic.
    •      Does not extend to 3D: the 3D nature of
           objects can rarely be represented by
           silhouettes. (The 3D equivalent of binary
           processing uses voxels, spatial occupancy
           of small cubes in 3D space).
    •      Specialised lighting is required for
           silhouettes: it is difficult to obtain reliable
                                                                                                                  a final year
                                                                                                    M       e       h       a           G       a       r   g       ,




           binary images without restricting the                                                    student at Lingaya’s
           environment. The simplest example is an                                                  Institute of Mgt. & Tech.,
                                                                                                    Faridabad, Haryana,
           overhead projector or light box.                                                         India. Her areas of
                                                                                                    interest include Image
                                                                                                    processing and Artificial
    VIII.      CONCLUSIONS AND FUTURE                                                               Neural Networks. She
               WORK                                                                                 has published a paper in
                                                                                                    national and another in
                                                                                                    international conference
In this paper, the threshold determined is user-                                                    during her BE level.

independent. It will produce exact binary images
irrespective of the skin colour of the sign language
user.                                                                                                   P       r       a




                                                                                                                           a final
                                                                                                                                t   e       e       k           D       h   a   w   a   n   ,




                                                                                                        year student at Lingaya’s
                                                                                                        Institute of Mgt. & Tech.,
The JPEG images that have been taken for
                                                                                                        Faridabad, Haryana, India.
processing have been clicked from a fixed distance,
                                                                                                        His areas of interest
keeping the camera position fixed too.
                                                                                                        include Image processing,
Future work includes removing these constraints
                                                                                                        Artificial Neural Networks,
for distance and camera position and forming clear
                                                                                                        Computer organization and
binary images without distortions. It will focus on
                                                                                                        Operating System.
the extension of the developed modules in order to
support larger vocabularies and enable more
natural communication of the users.




                                                             77                             http://sites.google.com/site/ijcsis/
                                                                                            ISSN 1947-5500

				
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