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Studying the effect of lossy compression on image fusion

Abstract: Image fusion is the process of merging two different types of images for the same region to get one image. There are different techniques for image fusion like Intensity Hue Saturation (IHS), Principal Component Analysis (PCA), High Pass Filter  (HPF). The raw images and the fused image usually require a large storage to save them, so it will be difficult to save them on the Internet. Here is the aim of image compression is to reduce the size of the image to be able to save it on the Internet, so This Research is talking about the effect of lossy compression on High Pass Filter image fusion.  

Keywords: lossy compression, MrSid, image fusion, HPF, RMSE

1. Introduction:

From remote sensing, we can get two types of images. One of the them is called the panchromatic image (pan) with high spatial resolution and low spectral resolution, and the other one is called the multispectral image (mul) with high spectral resolution and low spatial resolution. The images that were used in this research are for Cape Town, South Africa. The pixel of the pan image is 0.5m*0.5m and the pixel of the mul image is 2.0m*2.0m. The image fusion is the process to merge these two images into one image with high spatial resolution and high spectral resolution. The method of image fusion used in this research is high pass filter (HPF). After that we compressed the two raw images with different compression ratio (1:10, 1:20, 1:30, .......... 1:100) by using the Discrete Wavelet Transformation (MrSid format), and then we fused the compressed raw images to get 10 fused images. Finally, we compared between the fused image that came from the two raw images and the 10 fused images those came from the compressed raw images with different compression ratios.

2. Related work:

2.1. Image fusion:

2.1.1. High Pass Filter (HPF):

A high-pass filter (HPF)  passes high-frequency signals, as it is an electronic filter, but it lows the  amplitude of signals with frequencies lower than the cutoff frequency. The actual quantity of attenuation for each frequency  has variation from filter to filter. A high-pass filter is usually

represented as a linear time-invariant system [1].

At first, the panchromatic image which is the high spatial resolution is filtered by the high pass convolution filter which was made. The panchromatic image is filtered according to the multispectral image\'s size. After that the high pass filter image is supplemented to each band of the multispectral image so, we get the high pass filter fused image. At the final step, we compare between the high pass filter fused image with the multispectral image to see to what extent the results are acceptable. The result are got by comparing the standard deviation and the mean of the high pass filter fused image with the mean and standard deviation of the original multispectral image.[2][3]    

2.2. Image compression:

2.2.1 Image compression based on Discrete Wavelet Transformation (MrSid format):

Because of the development of the technology, the panchromatic image has become containing more spatial details and also more pixels and the multispectral image contains more spectral information. For the previous reasons, the panchromatic and the multispectral images\' size are larger than the past ones, so they need large space to save them on the internet or on any data base. Here the aim of image compression is obvious[4][5].

There are two types of image compression. These two types are loosy compression and lossless compression. The loosy and lossless compression mean that whether if an image loses data when it is compressed and then decompressed or not[6].

Lossless compression is the technique of compressing the image and when the compressed image is decompressed, the resultant image don\'t lose any data and it is similar to the original image, so this method is preferred is some applications especially for the medical images because if there is any losing data that will have a negative influence on the applications because it causes an incorrect analysis for the resultant image [7].  

Loosy compression is the other technique of the image compression. After decompressing the compressed image, the resultant image differs from the original image because of losing data and information. However, this type of compression is preferred in some applications because it allows different compression ratios so that we can get the compressed image in different sizes according to the allowable space to save the image[8][9]. This type of compression is used in this research.

There are many formats for the lossy compressed image like MrSID and JPEG 2000. The format used in this research is the MrSID format. MrSID is abbreviation for Multiresolution Seamless Image Database. It was designed by LizardTech. The MrSID algorithm is based on the wavelet transformation. Due to the developments in MrSID, we can compress the original image by high compression ratios and get effective and high quality of the image and high performance for the analysis of the compressed image which meet the requirements and the challenges of the industries and the researches[10][11].   

2.3. Evaluating the effect of lossy compression on image fusion:

Evaluating the effect of lossy compression on image fusion depends on the success of the protection of the spectral characteristics and the refinement of the spatial resolution. The visual comparison is done for the images. This type of comparison is depending on the human\'s eye to interpret the image and it may be subjective, but  there are techniques for evaluation based on statistical equations. These techniques are used for comparing among the different images after applying compression with different compression ratios and for measuring the color distortion among them because the power of the visual cognition as a final backdrop cannot be underestimated. These methods should be objective, reproducible, and of quantitative nature.  The RMSE is calculated as the difference between the mean and the standard deviation of the compressed images and the original fused image. The best value is to be equal zero. The following equation shows how to calculate the RMSE:

  RMSE=√((〖std〗_i-〖std〗_f )^2+(〖mean〗_i-〖mean〗_f )^2 )   .......................... (1)

3. Results and analysis:

we compared the fused image which came from the decompressed raw pan and mul images with the 10 fused images which came from the compressed pan and mul images with different compression ratios to get the RMSE to study the effect of compression on image fusion, as shown in table1.

Compres-sion ratio \"1:10\" \"1:20\" \"1:30\" \"1:40\" \"1:50\" \"1:60\" \"1:70\" \"1:80\" \"1:90\" \"1:100\"

RMSE 0.339054 1.22093 2.517272 3.247004 4.847105 5.286009 6.847213 7.26097 7.954781 8.019851

Table 1 illustrates the RMSE for the fused image came from the raw images and

the 10 fused images came from the compressed images  

From chart 1, it is obvious that compression ratio 1:10 gives the least RMSE as the RMSE equals 0.339 and the RMSE increases as the compression ratio increases from 1:20 to 1:100. The compression ratio 1:100 gives the highest RMSE.

Chart 1 illustrates the RMSE for the fused image came from the raw images and

the 10 fused images came from the compressed images  

4. Conclusion:

From the results it was found that the compression ratio 1:10 is the best and the perfect ratio that has a little effect on image fusion as it gives the lowest RMSE and the RMSE equals 0.339, while the other compression ratios (1:20, 1:30, ..... 1:100) give higher RMSE as it increases by increasing the compression ratios from 1:20 to 1:100 that gives the highest RMSE and the compression ratio 1:100 has the worst effect on the image fusion. We can make use of that in many applications of remote sensing. For example, we can make use of that in classification of palins, hills, and mountains as the best results for kappa coefficient when the compression ratio is less than 1:10 [12]. For the classification of crops and forests, the best compression ratio is from 1:3.33 to 1:5.

5. References:

   [1] Watkinson, John (1998). The Art of Sound Reproduction. Focal Press. pp. 268, 479.     


                            ISBN 0-240-51512-9. Retrieved March 9, 2010.

[2] Main, Bruce (February 16, 2010). \"Cut \'Em Off At The Pass: Effective Uses Of High-Pass

        Filtering\". Live Sound International (Framingham, Massachusetts: ProSoundWeb, EH


                      (a)                                                                              (b)


Figure 1 illustrates the original images (a) multispectral image, and (b) panchromatic image

Figure 2 illustrates the HPF fused image form the raw pan and mul images

[3]  Paul M. Mather (2004). Computer processing of remotely sensed images: an introduction     

         (3rd ed.). John Wiley and Sons. p. 181. ISBN 978-0-470-84919-4.

    [4] Lau, W.L., Li, Z.L. and Lam, K. W., 2003, Effects of JPEG compression on image

                         classification, International Journal of Remote Sensing, Vol. 24, No.7, pp. 1535-    


    [5] Zabala, A. and Pons, X., 2011, Effects of lossy compression on remote sensing image

                      classification of forest areas, International Journal of Applied Earth Observation and  

                      Geoinformation, Vol. 13, pp. 43–51

    [6] Zhai, L., Tang, X. M., Zhang, G., and Wu, X., 2008, Effects of JPEG2000 and SPIHT

                   compression on image classification, The International Archives of the

                   Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, China,

                  Vol. XXXVII, Part B7, pp. 541–544.

    [7] M. Mozammel Hoque Chowdhury and Amina Khatun, 2012, Image Compression Using

                                  Discrete Wavelet Transform, (IJCSI) International Journal of Computer

                                  Science Issues, Vol. 9, Issue 4, No. 1, pp. 327 – 330

     [8] Shrestha, B., O’Hara, G. C., and Younan, H. N., 2005, JPEG2000: Image quality metrics,

                         ASPRS 2005 Annual Conference, Geospatial Goes Global: From Your

                         Neighborhood to the Whole Planet, Baltimore, Maryland.

    [9] Campbell, J.B., 1996, Introduction to Remote Sensing, 2nd ed., Guilford, New York.

    [10] ERDAS, 2010, ERDAS Field Guide, ERDAS, Inc., Norcross, GA.

    [11] LizardTech, 2010, LizardTech’s MrSID Technology, Celartem Inc. dba LizardTech, Seattle,  

                              Washington, USA.

[12]  Zhai Liang and Tang Xinminga  , a Key Laboratory of Geo-Informatics of State Bureau of

        Surveying and Mapping, Chinese Academy of Surveying and Mapping, China -

          (zhailiang, tang),  Zhang Guob State Key Laboratory of Information

          Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan,

          430079, [email protected] , Wu Xiaoliangc  CSIRO Mathematical and

          Information Sciences, Private Bag 5, Wembley, Australia- [email protected]

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