Computer Science 1033A/B Lecture Notes - Lecture 2: Upsampling, Interpolation, Ellipse

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Published on 21 Apr 2013
School
Western University
Department
Computer Science
Course
Computer Science 1033A/B
Photoshop Basics 4/20/2013 5:53:00 PM
Lecture 2
Recap
All photo images (from camera, scanner) are bitmapped images
(.jpg .gif .png)
Bitmapped images (Raster images):
o Smallest unit of an image is the pixel (square)
o Blocky when increase in size
Vector images:
o Are scalable-will not be blocky if you increase or decrease
size
o Resolution independent takes on resolution of output device
o Vector images based on a mathematical relationship, but
are scalable, will not get blocky
Images: Resizing Images vs. Resampling
To Change Image Dimensions:
o Select Image> Size
o Provides two measurements:
Pixel Dimensions
Document size
Resampling (for Web)
You are physically changing the number of pixels in the image for
the web, adding or taking away pixels
Pixel Dimensions = web
o Width/height of our image in pixels
Width = 2048 Height=1536
Total # of pixels = 2048 x 1536 = 3,145,728
o To change simply type new values
Remember when going from smaller larger
dimensions you will get blurriness/pixilation
The higher the image dimensions to begin, the better
the outcome
Downsampling scaling it down
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o Decimation eliminating pixels and therefore deleting
information and detail from your image by averaging the
values of source pixels contributing to each output pixel
Casts off unnecessary information by averaging
together pairs of pixels or groups of pixels
Results in faithful tonality image still looks smooth
Uses the bicubic sharper option***
Upsampling scaling it up:
o Interpolation adding pixels by analyzing the colours of the
original pixels and “manufacturing” new pixels which are then
added to the existing ones
Guesses the values of the unknown pixels
6 interpolation methods:
Nearest neighbour:
Most basic/least sophisticated but fastest
Takes pixel color and assigns it to the new
pixels that are created (uses the same
color)
Poor quality, jaggedness
Bilinear:
Averages the color of the 4 pixels around it
and performs simple linear calculations
Less jaggedness but still not good quality
Bicubic (default):
Averages the color of the 16 pixels, a larger
sample size to draw from
Most sophisticated algorithm to give great
results - most commonly used
Best for smooth gradients
Bicubic smoother:
Great for upsampling
Averages from more than 16 pixels
Adds a blurriness pass but great photos
Bicubic Sharper:
Good method for downsampling image
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Document Summary

All photo images (from camera, scanner) are bitmapped images (. jpg . gif . png) Bitmapped images (raster images): smallest unit of an image is the pixel (square, blocky when increase in size. Vector images: are scalable-will not be blocky if you increase or decrease size, resolution independent takes on resolution of output device, vector images based on a mathematical relationship, but are scalable, will not get blocky. To change image dimensions: select image> size, provides two measurements: You are physically changing the number of pixels in the image for the web, adding or taking away pixels. Pixel dimensions = web: width/height of our image in pixels. Total # of pixels = 2048 x 1536 = 3,145,728: to change simply type new values. Remember when going from smaller larger dimensions you will get blurriness/pixilation. The higher the image dimensions to begin, the better the outcome.

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