MATLAB code for DCT based Color Image Compression.

Number of bits required to represent the information in an image can be minimized by removing the redundancy present in it

There are three types of redundancies:

1. Spatial redundancy

Which is due to the correlation or dependence between neighboring pixel values;

2. Spectral redundancy,

Which is due to the correlation between different color planes or spectral bands;

3. Temporal redundancy,

Which is present because of correlation between different frames in images.

Image compression research aims to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies as much as possible. Data redundancy is of central issue in digital image compression. If n1 and n2 denote the number of information carrying units in original and compressed image respectively, then the compression ratio CR can be defined as

Block Diagram:

Compression:

Description:

1. This compression system is totally based on DCT.

2. This system used for compressing color images.

3. In second block there is conversion of color to grayscale image.

4. Then DCT is applied to an image.

5. In 5^th block all the DCT coefficients are sorted with sorting algorithm.

6. After sorting algorithm the repeated DCT coefficients are eliminated so size is reduced.

7. Then image is again rearranged for applying IDCT, and after applying IDCT finally compressed image is obtained.

In this algorithm for reducing no of DCT coefficients rate is required. Rate should be less than size of the image.

E.g. if size of the image is 400 row and 400 columns

Then rate should be

In this case rate varies from 1 to 16 because by putting value of rate from 1 to 16 above condition is true.

De-Compression:

Exact Inverse Process is applied at Receiver or decompression.

Peak-Signal to Noise Ratio (PSNR)

The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codec’s (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codec’s it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality). The PSNR is calculated by using following formula.

MATLAB Implementation: 

Main GUI figure…

Compressions GUI figure:

De-Compressions GUI figure:

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