IMAGE COMPRESSION TECHNIQUES IN WIRELESS MULTIMEDIA SENSOR NETWORKS

    This project is design based on the paper "An Analysis of Image Compression Techniques in Wireless Multimedia Sensor Networks". Wireless Multimedia Sensor Networks (WMSNs) provide realization of applications which are usable everywhere and address many fields like mobile health care, environmental surveillance and traffic monitoring. Large amount of data causes to traffic in memory resources, difficulties in operation, and excessive power consumption -which is the most important one - for every node while WMSNs transfer multimedia data during those applications. Those kinds of problems are vital for WMSNs which already have limited resources. Image compression can be one of the effective solutions to overcome those problems. In this project we design three compression methods such as SPIHT,EZW and EBCOT to compare the performance with respect to PSNR value.


Image compression techniques in WMSN

SPIHT Algorithm


3D-DWT

The values which is placed in the matrix form as shown in below figure  should be compressed by using SPIHT algorithm(encoding) and then the compressed value should be displayed.


    Single Level                 Two Level                 Three Level
  • Applying the 3D wavelet process and finding approximate, detail coefficients(HHH,HHL,HLH,HLL,LHH,LHL,LLH,LLL).
  • Applying the SPIHT encode process using approximate and detail coefficients.
  • After SPHIT encoding process, finding the compressed data,this compressed date is given as input to SPIHT decoding process.
  • Once SPIHT decoding process over and then finding approx, detail coefficients.
  • From this approx, detail coefficients applying 3D Inverse wavelet process.
  • Finally getting Decompress Image.

You can DOWNLOAD Priority encoder(SPIHT)Verilog HDL code and reference paper.Contact sales@verilogcourseteam.com to get full design files.

SIMULATION VIDEO DEMO                                                                                                 


                                                                                                                          


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