Image compression is the reduction or elimination of redundancy in data representation in order to achieve reduction in storage and communication cost.[1] Discrete cosine transform (DCT) is computationally intensive algorithm it has lot of electronics application. DCT transforms the information time or space domain into frequency domain to provide compact representation, fast transmission, memory saving.[1]. DCT is very effective due to symmetry and simplicity. Image consists of pixels depending on image size. Ex.:- for 8*8 images, maximum pixel size 0 to 255. This project is dealing with image compression, for that there is need to implement DCT process on a particular image which will reduce the pixel size of that image. For Grayscale image compression using DCT, initially selected 256*256 image and access or read this image into MATLAB for DCT computation, Then found that after DCT process over image on MATLAB, the pixels size get reduced, that means ultimately image get reduced. Similarly selection becomes modified for different images i.e. 64*64 and 8*8 images, and then got same compressed image after DCT image compression. Then, after DCT computation, need to do IDCT computation for reconstruction of image. Image reconstruction means whatever image pixels have compressed for a particular use need to decompress it for further use. This project calculates error image between original images and reconstructed image. This project used MATLAB-XILINX-MATAB approach. In this, the input image, need to compress ultimately, that access or read into MATLAB for DCT and IDCT process, whatever the image read into MATLAB, applied DCT process over it. For XILINX implementation, whatever original input image already read into MAT LAB, there is creation of image matrix. Similarly for 8*8 DCT image compression, whatever DCT process and related formula computed into MATAB, there is creation of DCT matrix. This original image and DCT matrix access into XILINX using MAPPING PROCESS. Then using matrix multiplication process generated all image compression and image reconstruction. Then finally, calculate error image between DCT and IDCT of MAT LAB and XILINX. The coding is simulated using XILINX 13.4 ISE and final error image shown through MATLAB 7.0.4.

 

Published In : IJCSN Journal Volume 3, Issue 1

Date of Publication : 01 February 2014

Pages : 102 - 109

Figures : 16

Tables : --

Publication Link : IJCSN-2014/3-1/Design-and-Implementation-of-8-8-DCT-for-Grayscale-Image-Compression

 

 

 

Mr. Amit D. Landge :

Mr. M.M. Deshmukh :

Mr. B. P. Pardhi :

Mr. S. A. Bagal :

 

 

 

 

 

 

 

Discrete Cosine Transform (DCT)

Inverse Discrete Cosine Transform (IDCT)

VERILOG Hardware Descriptive Language (VERILOG HDL)

Very High Speed Integrated Circuit Hardware Descriptive Language (VHDL)

p align="justify" class="style134 style142">Joint Photographic Expert Group (JPEG)

Every system made for certain application that is to create one system which will used to generate, move or to follow up certain task. Along with this, the important task is how our system will be useful for future scope. So to visualize this we have taken some concept, i.e. now a days there is one theme or we can say one modification view just come into existance i.e we know that there are old movies, that was came before 40 years. But this kind of movies, that time, captured by black and white display way so covert grayscale video that means black and white video covert into color picture video.

 

 

 

 

 

 

 

 

 

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[4] Jongsun Park Kaushik Roy A Low Complexity Reconfigurable DCT Architecture to Trade off Image Quality for Power Consumption Received:2 April 2007 / Revised: 16 January 2008 / Accepted: 30 April 2008 /Published online: 3 June 2008.

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