Analysing Assorted Window Sizes with LBG and KPE Codebook Generation Techniques for Grayscale Image Colorization

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					                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                  Vol. 9, No. 6 June 2011

 Analysing Assorted Window Sizes with LBG and
KPE Codebook Generation Techniques for Grayscale
              Image Colorization
    Dr. H. B.Kekre                   Dr. Tanuja K. Sarode                 Sudeep D. Thepade                     Ms. Supriya Kamoji
Sr. Professor,MPSTME,                   Asst. Professor,                    Asst. Professor,                        Sr.Lecturer,
 NMIMS Deemed-to-be                 Thadomal Shahani Engg.                    MPSTME,                          Fr.Conceicao Rodrigues
University,Vileparle (W),                   College,                     NMIMS Deemed-to-be                       College of Engg,
   Mumbai-56, India.                Bandra (W), Mumbai-50,              University,Vileparle (W),                   Bandra (W),
                                             India.                        Mumbai-56, India.                      Mumbai-50, India.
Abstract—This paper presents use of assorted window sizes and        Gray scale image is represented by only the luminance values
their impact on colorization of grayscale images using Vector        that can be matched between the two images. Because a single
Quantization (VQ) Code Book generation techniques. The               luminance value could represent entirely different parts of an
problem of coloring grayscale image has no exact solution.           image, the remaining values within the pixel’s neighborhood
Attempt is made to minimize the human efforts needed in
manually coloring grayscale images. Here human interaction is
                                                                     are used to guide the matching process. Once the pixel is
only to find reference image of similar type. The job of             matched, the color information is transferred but original
transferring color from reference image to grayscale is done by      luminance value is retained [2].
proposed techniques. Vector quantization algorithms Linde Buzo
and Gray Algorithm (LBG) and Kekre Proportionate Error               The details in color image can be utilized for analysis and study
(KPE) are used to generate color palette in RGB and Kekre’s LUV      of particular image in the applications like medical
color space. For colorization source color image is taken as
                                                                     tomography, information security, image segmentation, etc.
reference image which is divided into non overlapping pixel
windows. Initial clusters are formed using VQ algorithms LBG         Coloring of old Black and White movies and rare images of
and KPE, used to generate the color palette. Grayscale image         monuments, celebrities is one of the best applications which
which is to be colored is also divided in non overlapping pixel      give good feel and understanding.
windows. Every pixel window of gray image is compared with
color palette to get the nearest color values. Best match is found   In case of pseudo-coloring [3] where the mapping of luminance
using least mean squared error. To test the performance of these     values to color values is automatic, the choice of color map is
algorithms, color image is converted into gray scale image and the
same grayscale image is recolored back. Finally MSE of recolored
                                                                     commonly determined by human decision. The main concept of
image and original image is compared. Experiment is conducted        colorization techniques exploits textual information. The work
on both RGB and Kekre’s LUV color space for the different pixel      of Welsh et al , which is inspired by the color transfer [4] and
windows of size 1x2, 2x1, 2x2, 2x3, 3x2, 3x3, 1x3, 3x1, 2x4, 4x2,    by image analogies [5], examines the luminance values in the
1x4, 4x1. However Kekre’s LUV color space gives outstanding          neighborhood of each pixel in the target image and add to its
performance. For different pixel windows KPE with 1x2 and LBG        luminance the chromatic information of a pixel from a source
with 2x1 pixel window perform well with respect to image quality.    image with best neighborhoods matching .This technique works
                                                                     on images were differently colored regions give rise to distinct
   Keywords- Colorization , Pixel Window, ColorPalette, Vector
                                                                     textures otherwise, the user must specify rectangular swatches
Quantization(VQ) , LBG, KPE.
                                                                     indicating corresponding regions in the two images.
                                                                     Color traits transferred to gray scale images [6] presents novel
                       I.   INTRODUCTION                             coloring techniques where color palette is prepared using pixel
    Colors always provide more clear information than gray           windows of some degree taken from reference coloring image.
scale digital images. Colorization is the art of adding color to a   For every window of gray scale image the palette is searched
monochrome image or movie. Colors we perceive in an object           for equivalent color values which could be used to color gray
are determined by nature of light reflected from the object. Due     scale window [19].
to the structure of human eye, all colors are seen as variable
combinations three basic colors Red, Green, Blue (RGB). The              In this paper, adjacent pixels are grouped together to form a
task of coloring a grayscale image involves assigning RGB            (pixel window) grid. Vector Quantization algorithms LBG and
values to an image which varies along only the luminance             KPE are applied on different pixel window sizes 1x2, 2x1, 2x2,
value. Since different colors may have the same luminance but        2x3, 3x2, 3x3, 1x3, 3x1, 2x4, 4x2, 1x4, 4x1and codebook of size
vary in hue and saturation, the problem of coloring gray scale       512 is obtained. Vector Quantization algorithms LBG and KPE
needs human interaction [1].                                         are applied. Depending on minimum Euclidean distance, LUV

                                                                                                  ISSN 1947-5500
                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                   Vol. 9, No. 6 June 2011
components of reference image are transferred to input gray two vectors are generated by using constant error addition to the
image.                                                          codevector. Euclidean distances of all the training vectors are
                                                                computed with vectors v1 & v2 and two clusters are formed
             II. KEKRE’S LUV COLOR SPACE [15,21]                based on closest of v1 or v2. This modus operandi is replaced
                                                                for every cluster. The shortcoming of this algorithm is that the
    In the proposed technique Kekre’s LUV color space is used. cluster elongation is +135O to horizontal axis in two dimensional
Where L gives luminance and U and V gives chromaticity cases resulting in inefficient clustering.
values of color image. Positive values of U indicate prominence
of red components in color image and negative value of V
indicates prominence of green component. The RGB-to LUV
and LUV-to-RGB conversion matrices are given in equation 1
and 2 respectively.

⎡L ⎤     ⎡ 1 1 1⎤     ⎡R ⎤
⎢U ⎥ = ⎢− 2 1 1⎥ * ⎢G ⎥                                        (1)
⎢ ⎥      ⎢       ⎥    ⎢ ⎥
⎢V ⎥
⎣ ⎦      ⎢ 0 1 1⎥
         ⎣       ⎦    ⎢B ⎥
                      ⎣ ⎦
⎡ R ⎤ ⎡1 − 2 0⎤ ⎡ L / 3 ⎤                                                                Figure1 LBG for Two dimensional case.
⎢G ⎥ = ⎢1 1 1 ⎥ * ⎢U / 6⎥                                      (2)
                                                               B. Kekre’s Proportionate Error (KPE) Algorithm [9,10]
⎢ ⎥ ⎢          ⎥ ⎢      ⎥
⎢ B ⎥ ⎢1 1 1 ⎥ ⎢V / 2 ⎥                                              Here to generate two vectors v1 & v2 proportionate error is
⎣ ⎦ ⎣          ⎦ ⎣      ⎦                                      added to the codevector. Magnitude of elements of the
                                                               codevector decides the error ratio. Hereafter the procedure is
                                                               same as that of LBG. While adding proportionate error a safe
                 III. VECTOR QUANTIZATION                      guard is also introduced so that neither v1 nor v2 go beyond the
                                                               training vector space eliminating the disadvantage of the LBG.
    Vector Quantization (VQ) [7],[8] is an efficient and lossy Fig. 2, shows the cluster elongation after adding proportionate
technique for compression of data and has been successfully error.
used in various applications like an pattern recognition[11],
speech recognition and face detection[12][13],image
segmentation[14],speech data compression [16],content based
image retrieval CBIR[17],[18] etc.

     Vector Quantization can be define as a mapping function
that maps k-dimensional vector space to a finite set CB = {C1,
C2,C3, ..…., CN}. The set CB is called codebook consisting of
N number of codevectors and each codevector Ci= {ci1, ci2, ci3,
……, cik} is of dimension k. The key to VQ is the good
codebook. Codebook can be generated in spatial domain by                Figure 2 orientation of line joining two vectors v1 and v2 after addition of
clustering algorithms.                                                                      proportionate error to the centroid.

                                                                                     IV.    PROPOSED COLORING TECHNIQUE
     In color transfer phase, image is divided into non
overlapping blocks and each block then is converted to the           Since the coloring problem always requires human
training vector Xi = (xi1, xi2, ……., xik ). The codebook is then  interaction. So reference image of same class and of same
searched for the nearest codevector Cmin by computing squared     feature as of input grayscale image. The color transfer
Euclidian distance as presented in equation (3) with vector Xi    algorithm is discussed for LUV color space for different m x n
with all the codevectors of the codebook CB. This method is       pixel grid size. The main steps of algorithm for a color transfer
called exhaustive search (ES).                                    are:
d(Xi, Cmin) = min1≤j≤N{d(Xi,Cj)}                            (3)        • Convert RGB components of source color image into
where d(Xi,Cj) = ∑(Xip - Cjp)2                                              respective Kekre’s LUV color components.
It is obvious that, if the codebook size is increased to reduce the    • Divide the image in to blocks of m x n pixels. Hence
distortion the searching time will also increase.                           m x n x3 dimensional training vector set
   The following section describes the VQ codebook                          corresponding to LUV components of each pixel is
Generation Algorithms.                                                      obtained. On this set LBG and KPE algorithms are
                                                                            applied and color palette is generated i.e. codebook of
A. Linde Buzoand Gray Algorithms(LBG) [7,8]                                 size 512.
                                                                       •    The input gray image is divided in mxn blocks of
   In this algorithm centroid is first calculated by taking
                                                                            pixels. Each block (pixel window) is searched for
average as the first code vector for the training set. In figure1

                                                                                                         ISSN 1947-5500
                                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                              Vol. 9, No. 6 June 2011
     •    nearest code vector of color palette. While searching
          only luminance is compared
     •    Once the nearest match is obtained gray image pixel
          window is replaced by LUV codevector
     •    The final colored image in LUV domain is then
          converted into RGB plane and MSE of original color
          image and recolored image are calculated.
                                                                                         6(a)               6(b)                  6(c)               6(d)
                                                                                    Original image       Gray image
                             V.     RESULTS                                                                                 1x2 Grid LBG       1x2 Grid KPE
                                                                                                                               MSE: 92            MSE: 81
The algorithms discussed above are implemented using                                Figure 6 shows reconstruction of face grayscale image using similar source
                                                                                                          image for pixel window 1x2.
MATLAB 7.0 on Pentium IV, 1.66GHz, 1GB RAM. To test the
performance of these algorithms we have converted color
image to grayscale image and the same gray image is recolored
back. Finally MSE of original image and colored image is
compared. Five color images belonging to different classes of
size 128x128x3 are used.                                                                 7(a)          7(b)         7(c)          7(d)             7(d)
Figure3 to Figure6. Shows the results of LBG and KPE for                               Original      Reference      Gray        1x2 Grid      1x2 Grid KPE
Zebra, Book, Cartoon and Face images considering same image                             Image         Image        Image          LBG           MSE 709
                                                                                                                               MSE 990
as reference image.                                                                       Figure 7 shows reconstruction of Scenery grayscale image using
Figure7 and Figure8. Shows the results of LBG and KPE for                                                    different source image.
scenery and dog images considering different image as
reference image.

                                                                                         8(a)          8(b)          8(c)        8(d)           8(e)
                                                                                       Original      Reference    Gray Image   1x2Grid        1x2 Grid
                                                                                        Image         Image                      LBG        KPE MSE
                                                                                                                               MSE 303          285
                                                                                     Figure 8 shows reconstruction of Dog grayscale image using different
                                                                                     source image.
    3(a)               3(b)               3(c)                3(d)
  Original         Gray image        1x2 Grid LBG        1x2 Grid KPE                    Various images, each of size 128x128 pixels, were
   image                             MSE: 178.4          MSE: 73.85                  used to build the color palette, and their grayscale
  Figure 3 shows reconstruction of Zebra grayscale image using similar               equivalents were colored using color palette for various
                  source image for pixel window 1x2
                                                                                     pixel windows. The fig. 9, shows bar chart of average
                                                                                     mean squared error obtained across all five images with
                                                                                     respect to initial few pixel windows for RGB and Kekre’s
                                                                                     LUV color space. It is observed that, Kekre’s LUV color
                                                                                     space gives less MSE compared to RGB color space.
                                                                                     Hence in table 1 only Kekre’s LUV color space results for
                                                                                     different images using 12 varying pixel window
     4 (a)             4(b)                4(c)               4(d)                   are given.
Original Image      Gray Image        1x2GridLBG         1x2 Grid KPE
                                        MSE73.8            MS53.32
  Figure 4 shows reconstruction of book grayscale image using similar
                  source mage for pixel window 1x2

                                                                                        Figure 9– Average MSE across various Grid sizes for different color
     5(a)            5(b)               5(c)                5(d)
Original image    Gray image       1x2 Grid LBG        1x2 Grid KPE
                                     MSE: 1260          MSE:1023
Figure 5 shows reconstruction of cartoon grayscale image using similar
                 source mage for pixel window 1x2

                                                                                                                  ISSN 1947-5500
                                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                         Vol. 9, No. 6 June 2011

         Table I. Shows the Results of LBG and KPE for five color images from different categories of size 128x128x3.

            Input     VQ                                                   Grid Sizes
           Images     Alg.
                               1x2     2x1       2x2          2x3    3x2         3x3       2x4      4x2      1x3          3x1       1x4       4x1
           Image1    LBG      92.70    87.11    107.42    116.75    114.65       2302     5576     5652     107.32        106      122.29     114.
                     KPE      81.59    77.49    89.141    90.75     95.83        2122     5557     5663      87.32       89.09      91.41     87.3
           Image2    LBG      1260     1244      1056      1493      1420        6350     6928     9595      1251        1243       1532      1392
                     KPE      1023     1153      1363      1233      1150        6243     6725     9359      1278        1138       1013      1093
           Image3    LBG      178.4     147     440.90    721.73    675.15       2003     2486     4236       373         286      610.66     465.7
                     KPE      73.85    76.12    225.10    461.69    441.62       1823     2264     4663     143.5         142      273.38     226.0
           Image4    LBG      73.89    76.64    107.12    123.82    131.86       2340     3400     3664       90          97       116.92     128.9
                     KPE      53.32    52.73    79.094    103.43    95.439       2813     3371     3701     65.3          65       73.93      76.09
           Image5    LBG      1203     1244      1244      1406      1388        6340     7833     7916     1246         1240      1274       1266
                     KPE      1178     1174      1182      1414      1399        6384     7935     7968     1193         1175      1209       1212
            Average LBG       561.5    559.7      591     772.26    745.9        3867     5244     6212      613          594       731        673
            Average KPE       481.9    506.6     587.8    660.5     636.3        3877     5170     6270     553.4        521.8     532.1      538.8

From the data given in table1, it is seen that the performance                                                 REFERENCES
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two famous codebook generation algorithms alias LBG and                                 Generate Codebook for Vector Quantization," First International
KPE. For both the algorithms 12 assorted pixel window sizes are                         Conference on Emerging Trends in Engineering and Technology,
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considered for preparing the color palettes. As quality of                              July 2008, Available at online IEEE Xplore.
colorization is subjective to source color image and grayscale to                [11]   Ahmed A. Abdelwahab, Nora S. Muharram, "A Fast Codebook Design
be colorized image, the grayscale version of 5 color images are                         Algorithm Based on a Fuzzy Clustering Methodology", International
recolored using total 48 variations of proposed techniques with 2                       Journal of Image and Graphics, vol. 7, no. 2 pp. 291302, 2007.
color spaces (RGB and Kekre’s LUV), 12 pixel window sizes                        [12]   Chin-Chen Chang, Wen-Chuan Wu, "Fast Planar-Oriented Ripple
and 2 codebook generation techniques( LBG and KPE). The                                 Search Algorithm for Hyperspace VQ Codebook", IEEE Transaction
comparison of original color image and recolored image has                              on image processing, vol 16, no. 6, June 2007.
shown that Kekre’s LUV color space outperforms RGB color                         [13]   C. Garcia and G. Tziritas, "Face detection using quantized skin color
space. Further, it can be observed from results that unidirectional                     regions merging and wavelet           packet analysis," IEEE Trans.
                                                                                        Multimedia, vol. 1, no. 3, pp. 264-277, Sep. 1999.
pixel windows gives better colorization than bidirectional pixel
                                                                                 [14]   H. B. Kekre, Tanuja K. Sarode, Bhakti Raul, "Color Image
window sizes. The KPE performs better than LBG for                                      Segmentation using Kekre's Fast Codebook Generation Algorithm
colorization. In all the best performance is shown by KPE with                          Based on Energy Ordering Concept", ACM International Conference
1x2 window size in Kekre’s LUV color space.                                             on Advances in Computing, Communication and Control (ICAC3-
                                                                                        2009), 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg.,
                                                                                        Mumbai. Available on online ACM portal.

                                                                                                                     ISSN 1947-5500
                                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                               Vol. 9, No. 6 June 2011
  [15] Dr.H.B. Krkre, Sudeep D. Thepade, “Image Blending in Vista Creation         Dr. Tanuja K. Sarode has Received Bsc.(Mathematics) from Mumbai
       using Kekre’s LUV Color Space”, In Proc. Off PIT-IEEE Colloquium,                                      University     in    1996,     Bsc.Tech.(Computer
       Mumbai, Feb 4-5,2008.                                                                                  Technology) from Mumbai University in 1999,
  [16] H. B. Kekre, Tanuja K. Sarode, "Speech Data Compression using                                          M.E. (Computer Engineering) degree from Mumbai
       Vector Quantization", WASET International Journal of Computer and                                      University in 2004, Ph.D. from Mukesh Patel
       Information Science and Engineering (IJCISE), vol. 2, No. 4, 251254,                                   School of Technology, Management and
       Fall 2008. available:                                                     Engineering, SVKM’s NMIMS University, Vile-
  [17] H. B. Kekre, Ms. Tanuja K. Sarode, Sudeep D. Thepade, "Image                                           Parle (W), Mumbai, INDIA. She has more than 12
       Retrieval using Color-Texture Features from DCT on VQ Codevectors                                      years of experience in teaching. Currently working
       obtained by Kekre's Fast Codebook Generation", ICGST-International                                     as Assistant Professor in Dept. of Computer
       Journal on Graphics, Vision and Image Processing (GVIP),Volume 9,                                      Engineering at Thadomal Shahani Engineering
       Issue 5, pp.: 1-8, September 2009. Available online at                      College, Mumbai. Engineering, SVKM’s NMIMS University, Vile-Parle (W),                  Mumbai, INDIA. She has more than 12 years of experience in teaching.
                                                                                   Currently working as Assistant Professor in Dept. of Computer Engineering at
  [18] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, "Color-Texture
                                                                                   Thadomal Shahani Engineering College, Mumbai. She is life member of IETE,
       Feature based Image Retrieval using DCT applied on Kekre's Median
                                                                                   member of International Association of Engineers (IAENG) and International
       Codebook", International Journal on Imaging (IJI),Available online at
                                                                                   Association of Computer Science and Information Technology (IACSIT),
                                                                                   Singapore. Her areas of interest are Image Processing, Signal Processing and
  [19] Dr. H. B. Kekre, Sudeep D. Thepade, Nikita Bhandari, “Colorization of       Computer Graphics. She has 90 papers in National /International
       Gereyscale images using Kekre’s Bioorthogonal Color Spaces and              Conferences/journal to her credit.
       Kekre’s Fast Codebook Generation “,CSC Advances in Multimedia
       An international journal (AMU), volume 1, Issue 3,pp.49-58, Available       Sudeep D. Thepade has Received B.E.(Computer) degree from North
       at                                                                                                             Maharashtra University with Distinction in                                              2003. M.E. in Computer Engineering from
       MU-13.pdf.                                                                                                     University of Mumbai in 2008 with
  [20] Dr. H. B. Kekre, Sudeep D. Thepade,Adib Parkar, “A Comparison of                                               Distinction, currently submitted thesis for
       Harr Wavelets and Kekre’s Wavelets for Storing Color Information in                                            Ph.D. at SVKM’s NMIMS, Mumbai. He has
       a Greyscale Images”, International Journal of Computer                                                         more than 08 years of experience in
       Applications(IJCA), Volume 1, Number 11, December 2010,pp 32-38.                                               teaching and industry. He was Lecturer in
       Available at                                               Dept. of Information Technology at
       2186.                                                                                                          Thadomal Shahani Engineering College,
  [21] Dr. H. B. Kekre, Sudeep D. Thepade,Archana Athawale, Adib Parkar,                                              Bandra(w), Mumbai for nearly 04 years.
       “Using Assorted Color Spaces and pixel window sizes for Colorization                                           Currently working as Associate Professor in
       of Grayscale images’,ACM International Conferences and workshops            Computer Engineering at Mukesh Patel School of Technology Management
       on emerging Trends in Technology(ICWET 2010), Thakur College of             and Engineering, SVKM’s NMIMS, Vile Parle(w), Mumbai, INDIA. He is
       Engg. And Tech.,Mumbai,26-27 Feb 2010.                                      member of International Association of Engineers (IAENG) and International
  [22] H. B. Krekre,Sudeep Thepade, Adib Parkar, “A comparison of Kekre’s          Association of Computer Science and Information Technology (IACSIT),
       Fast Search and Exhaustive Search for various grid sizes used for           Singapore. He is member of International Advisory Committee for many
       coloring a Grayscale Image” Second International conference on signal       International Conferences. He is reviewer for various International Journals.
       Acquisition and Processing, (ICSAP2010), IACSIT,Banglore,pp.53-             His areas of interest are Image Processing Applications, Biometric
       57,9-10 Feb 2010.                                                           Identification. He has about 110 papers in National/International
                                                                                   Conferences/Journals to his credit with a Best Paper Award at International
                                                                                   Conference SSPCCIN-2008, Second Best Paper Award at ThinkQuest-2009
                          Author Biographies                                       National Level paper presentation competition for faculty, Best paper award at
                                                                                   Springer international conference ICCCT-2010 and second best research project
Dr. H. B. Kekre has received B.E. (Hons.) in Telecomm. Engineering. from           award at ‘Manshodhan-2010’.
                                 Jabalpur University in 1958, M.Tech
                                 (Industrial Electronics) from IIT Bombay in
                                 1960, M.S.Engg. (Electrical Engg.) from           Supriya Kamoji has received B.E. in Electronics and Communication
                                 University of Ottawa in 1965 and Ph.D.                                   Engineering with Distinction from Karnataka
                                 (System Identification) from IIT Bombay                                  University in 2001. Currently pursuing M.E. from
                                 in 1970 He has worked as Faculty of                                      Thadomal Shahani College of Engineering,
                                 Electrical Engg. and then HOD Computer                                   Mumbai, India. She has more than 8years of
                                 Science and Engg. at IIT Bombay. For 13                                  teaching experience. Currently working as an
                                 years he was working as a professor and head                             Senior Lecturer in Fr.Conceicao Rodrigues
                                 in the Department of Computer Engg. at                                   College of Engineering. Mumbai, India. She is a
Thadomal Shahani Engineering. College, Mumbai. Now he is Senior Professor                                 life time member of Indian society of Technical
at MPSTME, SVKM’s NMIMS. He has guided 17 Ph.Ds, more than 100                                            Education (ISTE). Her areas of interest are Image
M.E./M.Tech and several B.E./ B.Tech projects. His areas of interest are Digital    Processing, Computer Organization and Architecture and Distributed
Signal processing, Image Processing and Computer Networking. He has more            Computing.
than 270 papers in National / International Conferences and Journals to his
credit. He was Senior Member of IEEE. Presently He is Fellow of IETE and
Life Member of ISTE Recently 11 students working under his guidance have
received best paper awards. Two of his students have been awarded Ph. D. from
NMIMS University. Currently he is guiding ten Ph.D. students.

                                                                                                                     ISSN 1947-5500