Image Compression Comparative Analysis of Basic Algorithms

Document Sample
Image Compression Comparative Analysis of Basic Algorithms Powered By Docstoc
					    Image Compression:
 Comparative Analysis of Basic
        Algorithms

      Yevgeniya Sulema (Ukraine)
     Samira Ebrahimi Kahou (Iran)

National Technical University of Ukraine
      “Kyiv Polytechnic Institute”
      sulema@scs.ntu-kpi.kiev.ua
    samira_ebrahimi@hotmail.com
                    Outline

 Existing compression methods and classification
 Criteria
 How to choose image set for testing
 Realizing algorithms
 Getting numerical values on chosen criteria
 Verifying results obtained from test
 Analysis and conclusion
    Compression algorithms-Classification

5 Main Classification Types chosen.

By data type :

 General algorithms
 Algorithms for audio-compression
 Algorithms for image-compression
 Algorithms for video-compression
    Compression algorithms-Classification (..2)

By data source :

 Dynamic
 Static


By redundancy type :

 Statistical redundancy reduction
 Spatial redundancy reduction
     Compression algorithms-Classification (..3)
By restoring the original dataset:

 Lossless
 Lossy


By computational approach :

 Statistical
 Dictionary
 Transformation based
 Hybrid
             Classes of Images

 Business graphics (schemes, diagrams, charts)
 Pictures created in graphic editors (photoshop)
 Photorealistic images (photos, textures)


Coefficient of correlation can be used
between an analyzed (test) image and an etalon
image to classify images :

               covI A , I E 
           
              D( I A )  D( I E )
              Sample images




   Image with two monochrome areas
   Image with large monochrome fields
   Gradient image
   Image with small monochrome fields
              Correlation coefficients
                     (Sample images)


0.5    0.437366167

0.4

0.3                   0.239146336

0.2
                                    0.062622429
0.1

  0

-0.1

-0.2

-0.3
                                                  -0.300682476
-0.4
         image 1        image 2       image 3       image 4
                              Criteria
1.   Compression ratio
2.   Time of compression
3.   Time of decompression
4.   Peak signal-to-noise ratio (PSNR)

                     max( I ) 
       20  log10           
                        
                                      MSE : Mean Squared Error
                 n   m
                 pi, j  pi, j
         1                          2
     
        nm
                i 1 j 1


5.   Coefficient of correlation between original
     and decompressed image
Matlab image processing Toolbox
                Why Matlab?

 It provides a comprehensive set of reference-
  standard algorithms.
 The software is a collection of functions that
  extend the capability of the MATLAB.
 The toolbox supports a wide range of image
  processing operations.
 Most toolbox functions are written in the open
  MATLAB language, giving us the ability to
  inspect the algorithms, modify the source code.
               Algorithms:


Lossless :             Lossy :
   LZW                   JPEG
                           (Coarse and Fine)
   LZ77
                          Wavelet
   Huffman                (Daubechies, Coiflets, Symlets,
   Adaptive Huffman       Discrete Meyer wavelet,
                           Biorthogonal, Reverse
   Shannon-Fano           Biorthogonal)
   Arithmetic            SPIHT
                          Fractal
                                 Lossless
                            Time of compression
                                                                                                          102.6
100                                                                                                91.8
                                                                               87.0
                                                                        78.8
80
                                                  63.2
                                           59.6
60


40
                    25.7

20           12.2                                                                           11.8
                                                         4.8                          5.8                         6.6
                           1.9       3.7
       0.7                                                        0.9

 0
             Image 1                       Image 2                      Image 3                    Image 4



      LZ77     Huffman           ShannonFano         Arithmetic
                              Lossless
                       Time of decompression
                                                                                         113.8



100


80
                                       63.8

60                                                               51.6



40
                                                                                                 20.2
                                                                        15.9
20                                            10.8
       0.8 0.6 1.1 2.2       1.9 2.6                   0.8 2.8                 3.5 4.0

 0
             Image 1             Image 2                   Image 3                 Image 4



      LZ77    Huffman    ShannonFano      Arithmetic
                                 Lossless
                              Compression ratio
10
                       9.0
9
             8.0 8.0
8      7.3                                              7.1

7

6
5
4

3

2                                 1.4 1.5 1.4 1.5
                                                              1.1 1.1 1.2         1.0 1.0 1.0
                                                                            0.7
1

0
             Image 1                  Image 2                 Image 3             Image 4


     LZ77     Huffman        ShannonFano   Arithmetic
Lossless Algorithm Observation
Dictionary Based Algorithms most Effective

   LZ77 – prime example from our research

 Minimal Time for Compression
 Minimal Time for Decompression
 High Compression Ratio
                               Lossy
                        Time of compression
                      157.0                  154.4                      153.6                 154.0


140

120

100

80

60

40

20
        0.1 0.1 0.7            0.1 0.2 0.9                0.1 0.1 0.7           0.1 0.4 1.1
 0
             Test1                  Test2                      Test3                 Test4



      JPEG Coarse      JPEG Fine   SPIHT        Fractal
                                  Lossy
                          Time of decompression
 1
                                                                                           0.9
0.9
0.8

0.7                                     0.6
              0.6         0.6                                                                          0.6
                                  0.5               0.5                        0.6               0.5
0.6
        0.5                                                    0.5 0.5               0.5
0.5

0.4                                           0.3
                                                                         0.3
0.3
                    0.2
0.2

0.1
 0
              Test1                     Test2                       Test3                  Test4



      JPEG Coarse         JPEG Fine     SPIHT        Fractal
                            Lossy
                        Compression ratio
        19.2                                                  18.8
20
               17.6
18
16

14                               12.3
                                                                     11.9
12

10

8

6                                                                                     4.8
                      4.0 4.0                 4.0 4.0                       4.0 4.0               4.0 4.0
                                        3.5
4
                                                                                            1.2
2

0
                Test1                   Test2                         Test3                 Test4



     JPEG Coarse          JPEG Fine     SPIHT           Fractal
                    Lossy
            Correlation coefficient
        1.0 1.0 1.0 1.0      0.9         1.0 1.0        1.0 1.0 1.0 1.0                     1.0
 1

0.9
0.8
                                                                                      0.7
0.7
                                   0.6
0.6
                                                                          0.5
0.5

0.4

0.3
0.2
0.1
                                                                                0.0
 0
             Test1                 Test2                     Test3              Test4



      JPEG Coarse    JPEG Fine     SPIHT      Fractal
                                            Lossy
                                            PSNR
                                                                                         99.2
100

90
80

70
60                                                                         53.5
                      48.1                                                        48.1
50                                                      41.1
               37.7          38.1                                   37.8
                                                 35.8
40      32.7                         31.0 29.8
                                                                                                27.6 26.2 27.7
30                                                                                                               21.7

20

10

 0
                Test1                      Test2                            Test3                     Test4



      JPEG Coarse             JPEG Fine   SPIHT           Fractal
Lossy Algorithm Observations
   Fractal Algorithm not practical.

   All remaining algorithms are Hybrid

   Combination of procedures can result in
    increased quality.
                  Conclusion
Our research allows us to draw 3 main conclusions:


   The selection of the proper compression
    algorithm for each image class should be made

   Hybrid algorithms, JPEG, can be modified in
    order to achieve better result

   Combination of a dictionary and transforms
    most promising.
              Thank YOU!



   Questions…???

    samira_ebrahimi@hotmail.com