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Digital Image Fundamentals

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  1. Digital Image Fundamentals Chapter 2

  2. KEY CONCEPTS • Sampling and quantizing in digitizing images • Pixels and image resolution • Image bit depth

  3. KEY CONCEPTS • How pixels, image resolution, and bit depth are related to sampling and quantizing • Color representation in digital images • Bit-mapped images versus vector graphics

  4. Natural Image (pretend)

  5. Capture: Scanner or Digital Camera

  6. Sampling How many pixels do you capture? Area the might have much detail Is reduced to one pixel “Averaging” effect

  7. Sampling Each Sample Point is translated into a pixel Real world image:Infinite detail Single Pixel:Finite detail

  8. Quality Loss illuminated • Once the digital image is captured…. • There is no way to “restore” the original detail.

  9. Analog / Real World Images • In the real world, we can perceive almost infinite detail • By… • Moving closer • Using a microscope • Nano-visualization

  10. Sampling: Number of Pixels Original 7500 pixels (100 X 75)

  11. Sampling: Number of Pixels Original 1900 pixels (50 X 38)

  12. Sampling: Number of Pixels Original 300 pixels (20 X 15)

  13. Sampling Scanners Digital Cameras Up to 12 Megapixels 4000 X 3000 pixels Consider 300dpi printing 13 X 10’ looks great Consider 150dpi printing 26 X 20” looks pixelated. • Up to 4800 pixels per inch • 8 X 10” photograph  • 38,400 pixels X 48,000  • 1,843,200,000  • 1.7 Gigapixels * 32-bit color • 6.8 Gigabytes

  14. Megapixel • Associated with digital cameras • Camera manufactures do NOT follow the base-2 standard so • a Megapixel is literally 1 million pixels • not 220 pixels. • A 3000 × 2000 pixel digital image is 6,000,000 pixels • Referred to as six megapixels.

  15. Pixels and Dots Pixel Dot Smallest unit that a printer can print. Smallest unit that a scanner can detect • Just the base unit of storage. • Does NOT have a set size, i.e., 1 millimeter X 1 millimeter • Pixel size depends on the monitor or projector • May not be a square • Could be a circle of light

  16. Over sampling Scanner Printer 300dp Printed as one dot • 4800dpi • 4800/300 = 16 • 16 X 16

  17. Under sampling Scanner Printer 600dpi 600/300 = 2 Printed using 2 X 2 NOT taking full advantage of the printers capabilities. • 300dpi • One dot

  18. Quantization: Number of Colors Original (16.7 million colors) 2 colors (1-bit)

  19. Quantization: Number of Colors Original (16.7 million colors) 4 colors (2-bit)

  20. Quantization: Number of Colors Original (16.7 million colors) 8 colors (3-bit)

  21. Quantization: Number of Colors Original (16.7 million colors) 16 colors (4-bit)

  22. Quantization: Number of Colors Original (16.7 million colors) 32 colors (5-bit)

  23. Quantization: Number of Colors Original (16.7 million colors) 64 colors (6-bit)

  24. Quantization: Number of Colors Original (16.7 million colors) 256 colors (8-bit)

  25. Quantization • A natural image is colored in continuous tones • theoretically has an infinite number of colors. • Binary representation restricts the reproduction colors and shades. • 8 bits can only encode 256 different color values • In image capturing, the process of encoding an infinite number of possible colors into a finite number list of colors is called quantization.

  26. Color selection • Quantization limits how many colors you can represent in a digital image, but • Not which colors you decide to encode. • Although this image has only 8 colors • The colors are very specific to the original image.

  27. Color Palettes Image formats (like GIF) can encode different palettes - depends on colors in the original image. If you chose the colors wisely the image looks more realistic.

  28. Color Translation Meta data “maps” each 3-bit code to a universal 32-bit palette 000  10101010101010101010101001010101 001  10101010101010100010010010010101 010  11010101001000111101010101010101 011  00010101010101010101111010100011 100  11101010111010111011010010100010 101  10101001000100010011110101010000 110  10101010100010110001111001101011 111  00100010001111010010000001111111

  29. Red Green Blue • A single 8-bit byte can be used to represent one of 256 different possible values. • The values range from 0 to 255. • An RGB (red, green, blue) color can be represented in three 8-bit bytes • Example: • Red: 255 (maximum red) 8-bits • Green: 0 (minimum green) 8-bits • Blue: 127 (50% blue) 8-bits24-bits total • What color is this?

  30. Red Green Blue • Although 24-bit color is sufficient for human vision, 48-bit RGB can be used when applications need to analyze images beyond the spectrum visible to humans. • 48-bit RGB color is represented using 16 bits per component of R, G, and B.

  31. 32-bit color • A 32-bit image is basically 24-bit RGB with an additional 8-bit alpha channel. • The alpha channel is used to specify the level of transparency. • Unlike 24-bit images that are fully opaque, 32-bit images can be smoothly blended with other images.

  32. Alpha-channel • Refers to the transparency of a color. • Transparency is a powerful feature because it better models real world entities. • Consider a digital image of a tinted window.

  33. Questions • How many possible colors can be represented with 48-bit color depth? • How many possible levels of red can be represented with 48-bit RGB color? • How many times would a file size increase by going from 24-bit to 48-bit?

  34. Bitmapped Images • Hopefully, lab helped illustrate this…

  35. Bitmapped Images But now consider that instead of these being 1’s and 0’s, they are 24-bit, 32-bit, or 48-bit color codes.

  36. Bitmaps  Raster Graphics • Bitmapped images also are called raster graphics • “rastering” refers to the way most video displays translate the images into a series of horizontal lines on the screen.  

  37. Bitmaps • Bitmap image formats are the most commonly used in image-editing applications. • However, bitmap appearance depends on the resolution of the output device • Bitmapped images can appear jagged and lose detail when they’re scaled onscreen or printed.

  38. Jagged images (or Jaggies) • A picture is worth a thousand words, right?

  39. Alternative to Bitmaps • Besides pixel by pixel representation, what other way could we store images digitally? • Hmmm? Think about it. • Math Power!

  40. Vector Graphics • Vector graphics is the use of geometrical primitives such as • points, • lines, • curves, and • polygon(s) • …to represent images.

  41. Vectors x2, y2 • points, • lines, • curves, and • polygon(s) • are all based upon mathematical equations. x, y coordinate x1, y1

  42. Splines (not in book) • A spline is a curve defined by piecewise polynomial functions.

  43. Fonts – The first vector images • vectors still appear smooth at higher magnification • rasterized graphic • rasterized graphic with anti-aliasing

  44. Fonts Font magnification example: g

  45. Rasterization Stored as vector Displayed as scalable raster Monitors and projects still display using pixels. To display a vector image, software has to convert the vector information into a temporary raster image. Called rendering or rasterization • Vectors are literally made up of mathematical formulas • No pixels at all • In principle, vectors can be rendered at limitless resolution

  46. Aliasing (formal word for jagged) • Rasterized images will always appear jagged • You just have to zoom in. • This jagged effect is called aliasing • caused by under-sampling or over-maginfication.

  47. Anti-Aliasing • Pixels with intermediary shades can be used to soften the jaggedness • This technique is called anti-aliasing. • Technique to make rasterization more smooth.

  48. Raster (bitmap) and Vector formats Raster Vector SWF (Shockwave Flash) SVG (Scalable Vector Graphic) EPS (Encapsulated Postscript) AI (Adobe Illustrator) • GIF • JPEG • PICT • TIFF Combo-format (not in book) PNG (Portable Network Graphic) – Raster format but includes vector information when applicable.

  49. File Size • How much file space does a 6-megapixel 24-bit color image take up uncompressed. • 6,000,000 pixels × 24 bits per pixel • = 144,000,000 bits • File size in bytes: • 144,000,000 bits/(8 bits per byte) • = 18,000,000 bytes

  50. Obvious Compression • Reduce sampling • Lower the resolution • 3000 X 2000 (6 megapixels)  1500 X 1000 (3 megapixels • Lower the color depth • 24-bit color  16-bit color • Maybe there are only 20,000 different colors present