Imaging Techniques in Digital Cameras Presented by Jinyun Ren Jan. 29 2004.

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Presentation transcript:

Imaging Techniques in Digital Cameras Presented by Jinyun Ren Jan

2 Goals  Understand the basic operation of digital cameras  Tell the differences between digital cameras and film cameras  Study some terminologies related to digital cameras

3 Confused Market  Price( Canada $) vs. Megapixels

4 Components  Similar to 35mm film camera  Including lens, aperture and shutter  Already included: digital film Digital negative Digital development

5 Digital film--Image sensor  CCD or CMOS charge-couple device Complementary Metal Oxide Semiconductor  Usually CCD  Made of millions of photosensitive diodes photosite  Each photosite captures a single pixel in final image

6 Black and White  Image sensor can only capture brightness  Resulting a gray scale image  Where are all colors from?

7 What is color? RGBRGB CYMCYM

8 Color Filter Array (CFA)  between CCD and lens  cover each photosite by one color in terms of certain pattern  Filter out all but the chosen color for that pixel  Obtain an image containing intensity values of basic colors

9 Digital Negative--RAW  Data directly from image sensor  Without any in-camera process  contains the full range of tone and color information captured by image sensor  Camera related– You can’t change!  Final image depends on how you digitally “develop” it

10 Developing --true color  True color comes from interpolation based on neighboring pixels "I'm bright green and the red and blue pixels around me are also bright so that must mean I'm really a white pixel."

11 Developing --true color (cont)  Operation  demosaicing algorithm ++=

12 Resolution  Defined as X pixels times Y pixels of an image 1024X768  Equal to total pixels of CCD

13 Resolution example  Different resolution  The same quality  Determine the size of images  Has nothing to do with image quality

14 Image Quality  On a basis of the same resolution  A subjective term Good qualitypoor quality

15 Why Compression?  24 bits color  3 bytes per pixel  File size is huge without compression 1024x768=786,432  2.4M 2592x1944=5,034,960  15M  Requiring to reduce file size in order to convenient operation

16 Image compression  Lossless TIFF or RAW Files remain quite large  Lossy JPEG Control file size by choosing compression levels A process to degrade the image quality

17 What is “Megapixels”?  A marketing term to resolution 1-megapixel x768=786,432 2-megapixel x1200=1,920,000 3-megapixel x1536=3,145,728 4-megapixel x1632=4,021,248 5-megapixel x1944=5,034,960  Larger megapixel  larger image size  larger file size  more storage

18 Digital Pictures Usage  Displaying on computer monitor Resolution: 1600x1200=1,920,000  Print on 6”x4” paper with top quality Resolution: 1280x960=1,228,800  to your friends Around 600K (after compression)  daily use  2 megapixel is enough  Don’t burn too much money on Megapixel!!

19 Summary—how to choose  Lens, aperture and shutter are very important  Pay more attention to “digital film”, “digital negative” and “digital development methods”  Don’t get confused by “Megapixel”

20 Time is up! Q&A

21 References  ng/contents.htm ng/contents.htm  ollateral/digital_camera.pdf ollateral/digital_camera.pdf  ary/ ary/