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LabVIEW 3.2 Image Sampling
In Lab 3.1 we explored the effects of quantization on image quality and on image storage space measured in bits. Now we will discover that selecting the correct number of pixels in an image also has a big impact on image quality and image storage. This is called sampling. Both sampling and quantization are necessary if we want to represent an image as numbers. || Note: You will have to zoom back in on the downsampled image to view it properly. || || Pay special attention to the areas where the color of the neighboring objects changes abruptly -The sky There is little visual difference -The top edge of the small building The pixels aren't smooth -The sides of the small building The vertical windows have become distorted -The side of the large building Towards the top the windows appear bent -The palm trees There is little visual difference || 173400:6291456 Is the loss of image quality worth the savings in storage required if: -You are making a photograph to place in a scrapbook? No -You are posting the image on a web page? No -You are transmitting the image from Jupiter to Earth at 300 bits per second? Yes || || 693600 :6291456 Is the loss of image quality worth the savings in storage required if: -You are making a photograph to place in a scrapbook? Yes -You are posting the image on a web page? Yes -You are transmitting the image from Jupiter to Earth at 300 bits per second? No || ||
 * || Procedure ||
 * 1. || Start the Lab. ||
 * || The image of the striped building and the clouds should appear with the downsampling factor set to 1.
 * || Slowly increase the downsampling factor from 1 to 6 and observe the changes in the sampled image.
 * 2. || Change the downsampling factor to 6
 * || Q1: Examine the sampled image and note differences from the original image in the following areas:
 * || Q2: What is the ratio of the number of bits in the sampled image to the number of bits in the original image?
 * 3. || Change the downsampling factor to 3
 * || Q3: What is the ratio of the number of bits in the sampled image to the number of bits in the original image?
 * 4. || Change the downsampling factor to 12
 * || Q4:At a downsizing factor of 12 can you still tell what the objects in the image are?

Note the differences from the original image in the following areas: -The sky the sky and clouds have become more pixelated -The top edge of the small building the edge is pixelated -The sides of the small building distorted blur of black and white -The side of the large building blocky appearance -The palm trees visibly pixelated || appears to be the same color everywhere. How does the sampled image show you that there are small color variations in the sky in the original image? Because there are less pixels in the downsized image, there are less colors that show up.
 * || Q5: Look more closely at the sky in the original image and the sampled image. In the original image it

Use the cursor to read 5 different RGB values for five different pixels in the sky of the sampled image. Move the cursor to a pixel location and click the left mouse button to see the three RGB values. Record these values RGB value 1 = 0,79,131 (1,42) RGB value 2 = 147,180,218 (30,30) RGB value 3 = 234,235,235 (16,1) RGB value 4 = 190,196,171 (41,0) RGB value 5 = 159,164,138 (7,34) || yes, a small thumbnail or avatar || Why? Because in real life the number of pixels is infinite. If we can not have enough pixels for a perfect image, how do we decide how many pixels to use? We use as many as possible that can be conveniently stored and distributed. || Blocks of differing colors When does the orientation of the lines change? when the lines are diagonal ||
 * || Q6: Is an image with a downsizing sample of 12 sutable for any application?
 * 5. || Stop the Lab Close the Lab ||
 * || Do we ever have enough pixels to perfectly show all the details in an object? No
 * || If we do not use enough pixels what will the edges of the objects look like?