A digital image can be transformed into a set of coefficients using a reversible, linear transform, such as the discrete Fourier transform and the discrete cosine transform. The coefficients thus obtained can be quantized and coded for data compression. This is a lossy compression technique. It is called transform coding in the literature. transform coding technique using two linear transforms (the discrete Fourier transform and the discrete cosine transform) and discuss the information loss.
1. Write a computer program to implement the following transform coding schemes.
| Case 1 | Case 2 |
| Transform | Discrete Fourier Transform | Discrete Cosine Transform |
| Subimage size | 8 x 8 | 8 x 8 |
| Bit allocation | 8-largest coding | 8-largest coding |
| | | | |
You can use MATLAB or Visual C++ as the programming language. A sample program written in Visual C++ can be downloaded from.
2. Download ?€œ[url removed, login to view]; fro compress it using the above coding schemes, reconstruct the image by inverse transforms and show the decompressed image. Compare and contrast Case 1 and Case 2.
3. Compute the root-mean-square error between each original image and its transform-coded version. Compare and contrast Case 1 and Case 2.
4. Repeat Tasks 1 to 3 using 2-largest coding as the bit allocation method. Compare and contrast your results obtained from different coding schemes.
You can use MATLAB code or Visual C++ as the programming language
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