COMS 161 Introduction to Computing Title: Digitizing Images Date: February 21, 2005 Lecture Number: 16

Announcements Homework 4 Due 2/25/05

Review Digitization Sound Sampling Quantizing

Outline The nature of images How digital images are Natural vs. artificial images How digital images are Organized Created Stored Processed

The Nature of Images Natural images Artificial images From common, analog sources Photos, drawings, paintings, TV, movies, etc. Must be digitized for use with a computer Artificial images Generated digitally

Representing Digital Images Natural images (such as a photograph, a frame of a video, etc.) typically consist of continuous or analog signals Digital images are composed of pixels (picture elements) For use in a computer, natural images must be digitized

Paint by Numbers Digital images are similar to paint-by-number kits 1 2 3

Paint by Numbers Digital images are divided into a grid of square regions called pixels Real images do not have coloring book areas of uniform color

Paint by Numbers Even within a pixel the region will not be uniform 1 2 3 4 5

Example of Digitization Consider a photograph of a penny Pretend that this is a photograph To use this image in a computer, it must first be digitized

Example of Digitization The first step in digitizing this natural image is sampling This image is partitioned (sampled) into a 50×50 square grid of pixels The picture resolution of this digitized image will thus be 50×50

Example of Digitization An image’s aspect ratio is the ratio of the number of horizontal pixels to the number of vertical pixels This 50×50 grid has an aspect ratio of 1:1 Most computer screens are 1.33:1 (640×480, 1024×768, etc.) Std. TV is 4:3 (or 1.33:1) HDTV is 16:9 (or 1.78:1)

Example of Digitization The second step in digitizing the image is quantizing the pixels For each pixel, an average color is calculated This resolution (50×50) is ‘clearly’ insufficient to represent the detail of the original image

Resolution Picture resolution is a trade-off between image quality and file size This digitized image has a resolution of 272×416 Minimum file size is then (272×416) × (bytes/pixel) For 256 colors (one byte per pixel), minimum file size would be (272×416) × (1) = 110.5 KB For 16 million colors (3 bytes/pixel), it would be 331 KB

Resolution With the resolution reduced to 136×208, the picture loses detail File size is reduced to: 28.3 KB for 256 colors 84.8 KB for 16 million colors

Resolution With the resolution further reduced to 68×104, the picture becomes almost unrecognizable File size is greatly reduced to: 7.1 KB for 256 colors 21.2 KB for 16 million colors With large pictures and high color requirements, file size becomes very important Digital cameras can easily create single pictures larger than 1 MB

Quantizing Digital Images Imagine a simple image: a bright object on a dark background Sample the image as before Consider just a single row of pixels across the center

Quantizing Digital Images Assign number values to the pixels: 0 = ‘black’ 1 = ‘white’ Plot the values of the pixels on the center row 0.0 0.5 1.0 With this image, we only need two “colors”, black and white

Dynamic Range Most pictures are more complex than just black and white To adequately represent an image, we need enough levels of quantization to achieve the desired picture quality The range of values chosen for quantization is called the dynamic range of the digitized image

Dynamic Range Max value – min value Typically it is a power of 2 256 gray values = 28, 8 bits / pixel How large should a dynamic range be? Science says we can only distinguish between 40 different shades of gray!!

Dynamic Range Example This is a grayscale image quantized to 256 levels of gray 0 = ‘black’ 127 = ‘medium (50%) gray’ 255 = ‘white’ Dynamic range is sufficient for use in this presentation Clear detail in highlights and shadows

Dynamic Range Example The same image, now quantized to 16 levels 0 = ‘black’ 7 = ‘medium (50%) gray’ 15 = ‘white’ Dynamic range is acceptable Detail somewhat reduced in highlights and shadows False contours becoming apparent (especially on chin and cheeks)

Dynamic Range Example The same image, now quantized to 4 levels 0 = ‘black’ 1 = ‘dark (67%) gray’ 2 = ‘light (33%) gray’ 3 = ‘white’ Dynamic range is marginal Detail severely reduced Shadows flattened Extreme false contouring

Dynamic Range Example The same image, now quantized to 2 levels 0 = ‘black’ 1 = ‘white’ Dynamic range is unacceptable Detail almost gone But, this may be a desirable artistic effect

Dynamic Range Example