MODULE – I 1. Introduction to Digital Image and Its Representation 2. Image Processing to Computer Vision- Low Level, Mid-Level, High Level Image Processing with Examples 3. Sensing and Acquisition 4. Sampling and Quantization 5. Image Resolution and Storage; Convolution 6. Basic Relationship between Pixels 7. Monadic and Dyadic Operators TOPIC 5: IMAGE RESOLUTION, STORAGE, CONVOLUTION Grey Level of an Image k represents the no. of bits used to express grey level (pixel brightness) Due to processing, storage and sampling hardware consideration, the number of grey level is an integer power of 2 L = 2k For 8 bit per pixel, it is 28 = 256 For 6 bit per pixel, it is 26 = 64 For 4 bit per pixel, it is 24 = 16 For 2 bit per pixel, it is 21 = 2 Dynamic Range of an Image Range of values spanned by the grey scale is called the Dynamic Range of an image. Discrete levels are equally spaced and they are integers in the interval o to L-1 A High Contrast Image - Images whose grey scale occupies a significant portion of the grey scale having high Dynamic Range A Low Contrast image - Images whose grey scale occupies a small portion of the grey scale having low Dynamic Range Distinguish between Gray Level Resolution and Spatial Resolution Gray Level Resolution Gray-level Resolution of an image is a term that refers to the number of shades of gray that is used for displaying the image. Variations in gray-level resolution affect the appearance of the image Spatial Resolution Spatial Resolution of an image is the physical size of a pixel in that image; i.e., the area in the scene that is represented by a single pixel in that image Commonly available grey level of an image 8 bits are used in common applications, 256 grey levels 16 bits are used in specific applications, where enhancement of specific grey level range is required 10 or 12 bits are used in some rare application Zooming of Image Method 1: Replicating each row and each column a number of times Two Methods: - Nearest Neighbor Interpolation - Bilinear Interpolation Method 2: Replicating each pixel a number of times Zooming - Nearest neighbor Interpolation Zooming - Bilinear Interpolation Shrinking of Image Method 1: Deleting row and columns Method 2: Averaging pixel Shrinking – Deleting rows and columns npd Changing the grey level resolution of an image Image Resolution – Varying Grey Level Resolution Image Resolution – Varying Grey Level Resolution Image Resolution – Varying Grey Level Resolution Original image 256X256, subsequent images have size (256X256) At every step, images are obtained by reducing the no. of available grey levels from 6 bits to 1 bit Storage No. of bits required to store a Grey Scale and a Color Image M = No. of rows, N = No. of Columns, k= No. of Grey Level, b = No. of Buffers Total no. of bits required to store a digitized image Total = M X N X k X b If No. of rows = No. of Columns, M = N, Total = N2 Kb Example: For Grey Scale Image Image size: 512 x 512 No. of gray level = 8, Buffer for Grey Scale b = 1 No. of bits required 512 x 512 x 8 its Storage No. of bits required to store a Color Image Example: For Color Image Image size: 512 x 512 No. of gray level = 8, Buffer for Grey Scale b = 3 No. of bits required 512 x 512 x 8x3 bits Convolution Convolution - What it is? Convolution is a simple mathematical operation which is fundamental to many common image processing operators Two elements required for convolution are: Image: input image in the form of a 2D array Kernel or mask: two dimensional array much smaller in size. Size of the mask could be (3X3), (5X5), (7x7) etc. - W1, W2, ….. W9 are the mask coefficients for the 8 neighbors of (x, y) h[ f( x , y ) ] = w1f( x - 1 , y - 1 ) + w2f( x - 1 , y ) + w3f( x - 1 , y + 1) + w4f( x , y - 1) + w5f( x , y ) + w6f( x , y + 1 ) + w7f( x + 1 , y - 1 ) + w8f(x + 1 , y ) + w9 f(x + 1 , y + 1)