1. Image Compression Techniques
Presented By:
Palash RoyChowdhury
Sai Krishna Madhavaram
Mohammed Abdul Kareem

2. Introduction: Image Compression
Data compression algorithms applied exclusively to images
Reduces Cost for storage and transmission
Few compression types better suited for specific image types
Adjustable parameters to improve compression quality

3. Introduction: Image Compression
Image Compression types
Lossless
PNG
BMP
TIFF
Lossy
JPEG
JPEG 2000

4. Introduction: Image Compression
Direct cosine Transform (DCT) and Fast Frequency Transform (FFT)
Lossy type Image compression algorithms
Joint photographic Experts Group (JPEG) is one of the most widely used lossy type algorithms

5. Introduction: DCT
The transformation of a two-dimensional matrix of pixel values into an equivalent matrix of spatial frequency components
Once the equivalent matrix of spatial frequency components, known as coefficients, has been derived then any threshold can be dropped.

6. Introduction: DCT Our vision can perceive sharp edges better
Such regions are considered “High frequency zones”
DCT algorithms are useful for separating High and Low frequency zones effectively

7. Direct cosine Transform (DCT)

8. Direct cosine Transform (DCT)
Pixels in the source image are divided into multiple blocks of 8*8.
Each individual pixel position has a bit value between 0 to 255.
The purpose of using DCT is essentially to replicate these 8*8 blocks.

9. Direct cosine Transform (DCT)
Greyscale created by using cosine transforms.
Waves of different frequencies can be added and averaged to form these respective shades.
This process also has centering of the pixel value
Cosine Transform is then applied using 8*8 bases of Discrete Cosine Transform.

10. Direct cosine Transform (DCT)

11. JPEG STANDARD QUANTIZATION MATRIX
Varies based on the Quantization matrix
Allows us to filter out coefficients based on frequency
Large corporations like Adobe have their own proprietary matrix
JPEG STANDARD QUANTIZATION MATRIX

12. Decompression
DCT inverse gives back the shifted block but is centered at zero
Adding 128 will recenter the pixel values
Combining all the pixel blocks gives us back the compressed image.

13. Results - DCT

14. Fast fourier Transform
FFT uses Decimation in Time algorithm to reduce the computational time for DFT (Direct fourier transform) computation.
Details in the image are provided by high frequency components, but they are very susceptible to noise which causes spurious effects
Many filters in image processing are based on FFT

15. Elapsed time is 3.124373 seconds Elapsed time is 7.295931 seconds
Results - FFT
For DCT, time taken to compress 5 images with different compression ratio:
Elapsed time is seconds
For FFT, time taken to compress 3 images with different compression ratio:
Elapsed time is seconds

16. Thank you