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COMS 161 Introduction to Computing

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 Natural vs. artificial images How digital images are

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COMS 161 Introduction to Computing

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

  2. Announcements • Homework 4 • Due 2/25/05

  3. Review • Digitization • Sound • Sampling • Quantizing

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

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

  6. 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

  7. 1 3 2 3 1 1 2 3 Paint by Numbers • Digital images are similar to paint-by-number kits

  8. 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

  9. 1 3 2 2 4 4 3 2 5 2 2 5 2 2 1 5 Paint by Numbers • Even within a pixel the region will not be uniform

  10. 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

  11. 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

  12. 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)

  13. 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

  14. 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

  15. 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

  16. 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

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

  18. 1.0 0.5 0.0 Quantizing Digital Images • Assign number values to the pixels: 0 = ‘black’ 1 = ‘white’ • Plot the values of the pixels on the center row • With this image, we only need two “colors”, black and white

  19. 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

  20. 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!!

  21. 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

  22. 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)

  23. 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

  24. 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

  25. Dynamic Range Example

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