1 / 63

Digital Images

Digital Images. Art 321 Dr. J. R. Parker. How are images represented?. Computer images (digital) are stored as a 2D array or grid of values. The values are, of course, numbers. They are called picture elements, or pixels .

spencer
Download Presentation

Digital Images

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Digital Images • Art 321 • Dr. J. R. Parker

  2. How are images represented? • Computer images (digital) are stored as a 2D array or grid of values. • The values are, of course, numbers. They are called picture elements, or pixels. • The numbers represent the darkness or lightness, or the color, of the image at that point.

  3. How are images represented? 0 1 2 3 4 5 6 7 Column index • The indices represent the position of the pixel in terms of row (vertical or Y) and column (horizontal or X) position. 0 1 2 3 4 5 2,4

  4. How are images represented? 0 1 2 3 4 5 6 7 • Upper left is the origin (0,0) 0 1 2 3 4 5 2,4

  5. How are images represented? • In grade school math we drew graphs this way – it’s the same coordinate system, just upside-down.) Y X

  6. How are images represented? • Pixels are relative values. • A value of 5 might be light or dark depending on the possible range of values, and the protocol. • Protocol: smaller numbers are dark. 0 is darkest (black) • If the largest level is 8, then 5 is middle grey. If the largest is 255, then 5 is almost black.

  7. How are images represented?

  8. How are images represented?

  9. How are images represented? A ‘dead’ pixel.

  10. How are images represented? • Images are digitized from real scenes or photos. It is also called quantization – the conversion of greys into discrete numeric values. • The degree of quantization describes the number of possible levels. • One byte (8 bits) allows 256 levels, from 0 to 255. This is common. 32 is needed for image display.

  11. How are images represented? 16x16 levels 256 levels per row

  12. How are images represented? This image has 8 distinct levels. We can see contouring, where distinct regions meet.

  13. How are images represented? A few levels can convey a lot of information or feeling.

  14. How are images represented? • How many pixels are in an image is a measure of quality. It is the size of the 2D array (EG 256x256 pixels) • This is often called resolution, but really resolution is about how big a pixel is – what is it’s area in the real world? That is, how small an object can be seen. • Real-world example: how small an object can be seen on Mars from an orbiter?

  15. Resolution • 25 dpi (ppi) 6 dpi (ppi) To be really useful we need over 200 dpi

  16. Resolution • Here is a picture that is 1200 pixels wide by 800 high. How big do we print/display it?

  17. Resolution • At 72 pixels per inch, this image would be • 1200/72 = 16.67 inches by 800/72 = 11.11 inches. • If we do that we can see the pixels. The resolution is too low. • That’s why we can’t reasonably project computer images as large as we like – we can perceived the pixels if they are too big.

  18. Resolution Looks kind of blurred.

  19. Resolution • Reasonable quality is 200 ppi. • Professional is 300 ppi minimum. • At that resolution, the image would be • Width = 1200/300 = 4 inches • Height = 800/300 = 2.667 inches • A typical photo is 4x6 – what size do we need? • width/300 ppi = 6 -> width = 300 * 6 = 1800 • height/300 ppi = 4 -> height = 300 * 4 = 1200 • So we need 1800 x 1200 pixels, or 2,160,000 pixels

  20. Resolution • We can do this. My cheesy HP camera can give me 7.2 million pixels. • How about an 8x10? • DO THE WORK, FOLKS. You have 2 minutes.

  21. Resolution • Answer: • 300 pixels per inch x 8 inches wide = 2400 pixels • 300 pixels per inch x 10 inches wide = 3000 pixels • Total number of pixels = 2400 pixels wide x 3000 pixels high = 7,200,000 pixels • My cheesy camera wins!

  22. Resolution • Here’s a picture from my HP. 3072x2304 812 K JPEG

  23. Resolution • Here’s a picture from my Nikon. 2048x1536 585K JPEG

  24. Resolution • In defense of the Nikon, it takes pictures on film which are then scanned by the photofinisher. • They could be scanned at a much higher resolution. • The optics are much better too.

  25. Colour • Anyway, we now have resolution and quantization, and now should chat about colour. • Colour is stored (represented) how? • … wait for an answer.

  26. Colour • Right, the relative intensities of Red, Green, and Blue. • Just like on the TV screen, every pixel is specified as RGB values. Each one is 8 bits (0-255) so it takes 24 bits to store one pixel. • Our 7.2 megapixel image takes 21.6 million bytes to store in memory/on disk. • By the way, why RGB. Is there a physics thing involved, some kind of natural mathematical representation? • Nope.

  27. Colour • It is because our retinas se these three colours. There is essentially a little coloured oil droplet in front of each cone in our retina.

  28. Colour • Each animal has different retinal structure, and distinct colours of pigments. • This is a chicken – 2 colors.

  29. Colour • Right, the relative intensities of Red, Green, and Blue.

  30. Colour • As artists, you must know something about how colours mix. • EQ red and blue mix to purple • On a computer, we know exactly how much.

  31. Colour • Yellow = red + green • 255.255.128 255.255.64 255.255.32 255.255.0 Magenta = red + blue 255.128.255 255.64.255 255.0.255 Cyan = green + blue 128.255.255 64.255.255 0.255.255

  32. Colour • Changing a color value by one does not make much difference. • 128/129 128/119 But it depends on the colour. Our eyes are more sensitive to some colours than to others. 128/129 128/119

  33. Colour • http://homepages.ulb.ac.be/~dgonze/INFO/htmlcolors.png • This site shows you the colors as a table.

  34. Colour • There are lots of colours (How many?)

  35. Colour • Colours have names. HTML names, for example:

  36. Colour • There are many "neon" colors. • They have a common feature. They contain a chemical substance that absorbs ultraviolet radiation just at shorter wavelengths that visible light (about 400 nm) and re-emit light in the visible range of wavelengths (400-700 nm) [nm = nanometers]. Mixed with other dyes and pigments gives the "dazzling" colors. There are many web sites dealing with "fluorescence" • Can’t render them on a normal screen.

  37. Colour • So, a pixel can be 3 bytes, one for each colour • R, G, B. • Red = 126 green = 33 blue = 100 • Stored as a single number: • 126 + (256*33) + (65536 * 100) • Why? These are powers of 2. • 100 33 126 in hex is 64 21 7E • And thus in binary: • 01100100 00100001 01111110

  38. Colour • In the colour table, the colour ‘chocolate’ was defined as • #D2691E • Which is to say • RED = D2 = 210 • GREEN = 69 = 105 • BLUE = 1E = 31 • This is one reason we learned Hex – colours are specified using it.

  39. Colour • Converting from colour into grey scale is relatively simple. • 1. Can average the RGB values. • So chocolate becomes 210 + 105 + 31 = 356 • 346/3 = 119 as a grey level. • 2. Could simply choose R or G or B to represent grey. This is not recommended.

  40. Colour Frequency? Not really useful For design and computer stuff.

  41. Colour • Use a weighted average. This is useful for specific conversions, like NTSC television images. • grey = 0.213*R + 0.715*G + 0.072*B • NTSC is bad at representing blues.

  42. Colour • There are other ways to represent colour. • HIS or HSV (Hue/Saturation/Intensity) • Hue is the colour • Saturation is degree of colour • Intensity is brightness

  43. Colour • Hue goes from 0.0 to 1.0 or 0 to 255 • 255 is red, and is right next to 0 (circular) 0

  44. Colour

  45. Colour There are some basic rules for design using color that take advantage of the ‘hue circle’

  46. Colour The high contrast of complementary colors creates a vibrant look especially when used at full saturation. This color scheme must be managed well so it is not jarring. Complementary colors are really bad for text

  47. Colour Analogous color schemes use colors that are next to each other on the color wheel. They create serene and comfortable designs. Choose one color to dominate, a second to support. The third color is used (along with black, white or gray) as an accent.

  48. Colour A triadic color scheme uses colors that are evenly spaced around the color wheel. Triadic color harmonies tend to be quite vibrant, even with unsaturated versions of your hues. The colors should be carefully balanced - let one color dominate and use the two others for accent.

  49. Colour The split-complementary color scheme is a variation of the complementary color scheme. In addition to the base color, it uses the two colors adjacent to its complement. Often a good choice for beginners, because it is difficult to mess up.

  50. Colour Complementary Triad split-complementary

More Related