Page 97 - The-5th-MCAIT2021-eProceeding
P. 97

  
                                                                    ((2 )−1) 2
                                                                   = 10                         (3)
                                                                            
           Where MSE represents the Mean Squared Error, which is the average value of the combined square of errors,
        or  differences  in  each  pixel  between  the  original  and  compressed  image  (Sharma  2017).  The  equation  to
        measure MSE is as illustrated as follows:
                                                               1    
                                                                             2
                                                               =  ∑   (   −    ̂ )                                     (4)
                                                                              
                                                                          
                                                                     =1
           However, there are certain instances in which the MSE value of an image becomes inconsistent and hence a
        secondary measure of image quality, SSIM is introduced. SSIM measures image quality degradation between
        the original and compressed image by observing the perceivable structural differences between the two images
        and  serves  as  an  alternative  besides  PSNR  for  image  quality  measurement  (Wang  2004). The  equation  to
        measure SSIM is as illustrated as follows with μ x, μy representing the average of x and y, and σ x, σy representing
        the variance of x and y, and σ xy representing the covariance of x and y:
                                                                (2            +   1 )(2        +   2 )
                                                           (  ,   ) =   2  2  2  2            (5)
                                                               (      +      +   1 )(      +      +   2 )
        3. Findings

           After compression has been applied on the dataset, the Encoding Time,     CR     BPP     ,      PSNR      and
        SSIM) for each image using each algorithm was recorded in a table. The average value for these parameters
        were then calculated and tabulated in the table presented below.

        Table 1. Average compression results for each algorithm on a sample of 85 images












           From the table above, we can observe that the DWT algorithm produces the lowest performance out of all
        four algorithms that were tested. While the other three each excels in certain criteria. DCT produces a fast and
        a good quality compression, as seen with high PSNR and SSIM numbers, but with a lower CR compared to
        Huffman and SPIHT. Huffman on the other hand, is also fast and produces a much more compact compression
        as seen in the high CR numbers but produces a lower quality compression when compared to DCT and SPIHT.
        Amongst all the algorithms that were tested, SPIHT produced a very good overall result, being quite similar in
        performance  with  the  top  performing  algorithm  in  each  aspect,  resulting  in  a  high  CR,  PSNR  and  SSIM
        compression performance, with only one caveat, and that is the amount of time taken due to the complexity of
        the algorithm resulting in slow encoding speeds.

        4. Conclusion

           From the data that was gathered through this research, it can be clearly seen that image compression serves
        as an effective way to reduce the size of images, whether it be for more efficient storage, or for increased transfer
        efficiency. Based on the results, it can be said that the SPIHT algorithm performs well in all aspects besides
        encoding time. This can be a point to focus on in coming research to optimize the efficiency of the SPIHT








        E- Proceedings of The 5th International Multi-Conference on Artificial Intelligence Technology (MCAIT 2021)   [84]
        Artificial Intelligence in the 4th Industrial Revolution
   92   93   94   95   96   97   98   99   100   101   102