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Algorithm for lossy image compression using FPGA

05 Apr 2013  | K. Rajesh Kumar

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The strength of the weighted data in the image has been reduced by subtracting all the data's with the matrix mean and made into small clusters .The each clusters are parallel processed by taking the threshold and dividing it into smaller module's and assigned a weighted value. These individual clusters are concatenated and in the DCT, the concatenated image is divided into 8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each block. The DCT coefficients are then quantised, coded, and stored in the memory as compressed image .the forward steps are reversed to get the lossy image. Figures 2, 3 and 4 represent the original, compressed and reconstructed image.

Figure 2: Original image.


Figure 3: Compressed image.


Figure 4: Reconstructed image.


Figure 5: An example for the proposed scheme.



Performance comparison
In this section the proposed algorithm SME is compared with the existing techniques like the standard LMS,the normalized LMS (NLMS),the MVSS, the conventional TDLMS, the DCT-LMS, the TDVSS, and the VSSTDLMS. The DCT was selected as the orthogonal transform for all the simulations.

The following highly correlated input signal same as given in [9]-[11] was used


Where v (n) is uncorreleated Gaussian signal with zero mean and 0.14817 variance.


MSE
The MSE is the cumulative squared error between the compressed and the original image.

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