1. Image Compression via SVD
Ruchit Patel | Viggy Sak | Vedant Maheshwari

2. INTRODUCTION
Image processing is the study of how to store, transform and recompile the image.
Low­-level inputs and outputs being images.
Mid-­level the inputs are images, but outputs are attributes extracted.
High-level processing involves examination of an ensemble of recognised objects and performing the cognitive function associated with human vision.

3. PURPOSE
Singular Value Decomposition (SVD) is the factorisation of a matrix into several constitutive components. Transformation that compresses, stretches and/or rotates a given set of vectors.
Cropped (2480) and Uncropped (170) from The Extended Yale Face Database B.
Comparisons will be drawn between the SVD behaviour of the cropped and uncropped datasets.
Information withheld from the original images will be correlated to the number of modes / size of data considered.

4. PURPOSE
Cropped
Uncropped

5. BACKGROUND
Singular Value Decomposition

6. ALGORITHM IMPLEMENTATION

7. ANALYSIS

8. ANALYSIS

9. ANALYSIS

10. ANALYSIS

11. CONCLUSION
The results show that a decent image compression was achieved with the SVD algorithm as it was possible to perform reconstruction even using just a small percentage of total data.
Number of modes required to obtain a good quality image are arbitrary and are dependent on the nature of the data being assessed.
The SVD algorithm gives satisfactory results for both the cropped and uncropped image set, but the cropped image set provides a better compression ratio.

12. REFERENCES