1. Image Interpolator Supervisor By: Dr. Rajeev Srivastava Prashant Bhutani Associate Professor CSE, IIT-BHU

2. Abstract Image interpolation is the process of resizing or remapping an image. Image interpolation is a non-trivial process that involves a trade-off between efficiency, smoothness and sharpness. As the size of an image is increased, so the pixels which comprise the image become increasingly visible, making the image appear "soft". Conversely, reducing an image will tend to enhance its smoothness and apparent sharpness. Apart from fitting a smaller display area, image size is most commonly decreased in order to produce thumbnails.

3. Abstract…. Enlarging an image is generally less common. The main reason for this is that in "zooming" an image, it is not possible to discover any more information in the image than already exists, and image quality inevitably suffers. However, there are several methods of increasing the number of pixels that an image contains, which evens out the appearance of the original pixels. There are several interpolating algorithms which varies in the amount of blurring, aliasing, jagginess, etc produced in the interpolated image. Few of such algorithms are Nearest neighbour, Bilinear, Bicubic, Sinc, Spline, Adaptive image interpolation algorithms, etc.

4. Abstract… The aim of this project is to design a GUI framework which implements some of the image interpolation techniques to rescale an image. I have implemented nearest neighbour, bilinear, bicubic, spline and sinc algorithms which varies in their technique to produce interpolated images. The software is developed using Qt4, Qt Creator SDK-1.3.1, OpenCV -2.0.0 developed by Intel. Any user willing to view the work must have OpenCV library installed in their machine. With this, we should proceed with the introduction of various algorithms involved and illustrate various details of the implementation.

5. Image Interpolator What is Image Interpolator? A GUI framework designed in Qt4.6 which uses OpenCV2.0 library in the backend to implement some of the image interpolation algorithms. It allows user to choose one of the five algorithms (nearest neighbour, bilinear, bicubic, B-spline, cubic spline) to interpolate / resize the image. It allows user to select image from any directory of the system and save the interpolated image in any of the existing directories.

6. Image Interpolator Resources or Technologies used in the development of Image Interpolator are: Qt4.6 OpenCV2.0 library Image Interpolation Algorithms

7. Image Interpolator What is Qt? Qt (pronounced as 'Cute') is a C++ API developed by trolltech in 1994 and was bought by Nokia in 2006. Qt is a system independent API and is portable to any of the existing Operating Systems (windows, *unix, Macintosh) and also portble to Symbian OS. Above all, Qt is available under Open Source.

8. Image Interpolator What is OpenCV library? OpenCV is an acronym for Open Computer Vision Library. It is developed by INTEL and is available as an Open Source product. As the name suggests, it is developed mainly for the ease of development of computer vision / artificial intelligence products. As Computer Vision deals with objects in motion which in turn deal with static images running at some frequency, so the library makes it easy and efficient to deal with images.

9. Image Interpolator What is Image Interpolation? Image Interpolation is the process of resizing or remapping an image. Here we are dealing only with resizing of image. When size of an image is decreased, it enhances image's smoothness and sharpness. When size of an image is increased, the pixels which comprise the image become increasingly visible and image quality inevitably suffers. The Main reason for this is that in “zooming” an image, it is not possible to discover any more information in the image that already exists. There a lot of Image Interpolating algorithms which try to even the appearance of the original pixels on zooming the image. But the amount varies among algorithms. Since the applied algorithm is merely an approximation, therefore an image will always lose some quality on interpolation.

10. Image Interpolator Different Image interpolation algorithms used are: Nearest Neighbour Interpolation Bilinear Interpolation Bicubic Interpolation B-Spline Interpolation Cubic Spline Interpolation

11. Image Interpolation Nearest Neighbour Interpolation Sample method of multivariative interpolation in 1 or more dimensions. Simply selects the value of nearest point and neglects other neighbouring points. Thus, it yields a piecewise-constant interpolant. It is one of the easiest method to implement but the quality is worst. Algorithm do scaleheight= height of new image / height of original image do scalewidth= width of new image / width of original image for i=0 to height of the new image for j=0 to width of the new image for k=0 to original image colour channels do copy channel by channel data of nearest pixel of original image

12. Image Interpolator Bilinear Interpolation Extension of linear interpolation for interpolating functions of two variables on a regular grid. Key idea is to perform linear interpolation first in one direction, and then again in other direction. It gives better result than nearest neighbour but is slower than latter.

13. Image Interpolator Bicubic Interpolation It is an extension of cubic interpolation for interpolating data points on a two dimensional grid. It can be accomplished using either Lagrange Polynomials or Cubic Convolution algorithm. It is choosen over Nearest Neighbour and Bilinear interpolation as its interpolated surfaceis smoother than both of them and has fewer interpolation artifacts.

14. Image Interpolator Spline Interpolation A form of interpolation where interpolant is a special type of piecewise polynomial called a spline. Spline interpolation is preferred over polynomial interpolation because the interpolation error can be made small even when using the low degree polynomials for the spline.

15. Image Interpolator APPLICATIONS It can be used to interpolate an image to see the minute details. Thus, it has use in following fields: 1. Medical Image Processing 2. Normal Image Scaling like Adobe Photoshop, GIMP, etc.

16. Image Interpolator VISION To carry the project further to add many other functionalities such as: 1. Edge Detection 2. People Counter 3. Face detection 4. Image Segmentation

17. Image Interpolator Thank You EveryOne for their support and patience.