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Resolution Trade-offs
Lab Objectives In Lab 3.1 and 3.2 we explored the effects of changing quantization, which determines the number of bits per pixel, and changing the sampling interval, which changes the number of pixels in an image. Reducing either the number of pixels or the number of bits per pixel reduces the storage required for an image at a cost of lowered image quality.

In this laboratory we will explore how to stay within a "bit budget" for an image by adjusting both the quantization levels and the sampling to keep the best image quality. This can be important when deciding what images will be put on a web page or sent electronically

Verify that the image of the baboon on the right is the same as the image on the left and that the same number of bits is used for each image. || Remember to **zoom in** on the downsampled image so you can view it at the same size as the original image || 131072 What is the ratio of bits in the output image to bits in the input image? 524288:131072 || The fur texture What parts look different? The image appears less sharp. How would you characterize the parts of the image that show the most loss of quality? The darker shaded areas || Adjust the bits per pixel until you have an image using the same number of bits that you recorded in Ql || 2 || All the gray shading is gone Which image is better - this one or the one from Step 3? the one from Step 3 || Slowly reduce the number of bits per pixel until you just notice a further reduction in image quality || 3 bits per pixel How many bits are needed for the image? 49152 || In most cases the best quality will not be very good. Maximum bits in image pixel size bits per pixel 300,000 |- |- 327680 - The image is darker and a weaker in quality 200,000 |- |- 196608 - It's much darker and posterized 150,000 |- |- 131072 - It's extremely posterized, light and dark colors stand out 100,000 |- |- 98304 - It's image is less posterized and a tad pixelated 75,000 |- |- 81920 - It's slightly darker and more pixelated 50,000 |- |- 49152 - It's also slighter darker and more pixelated || Would our result be any different if we reduced the number of bits first and then downsampled? No || || 2 pixel/8 bits Is your choice different from the baboon image? No Why? The image is darker and unclear where there are less bits per pixel, and it's sharper and more legible with more bits per pixel. || In most cases the best quality will not be very good. Maximum bits in image pixel size bits per pixel 300,000 |- |- little darker 200,000 |- |- The image has brighter lights and darker shades. 150,000 |- |- It is starting to posterize 100,000 |- |- It's a tad blurrier than the original 75,000 |- |- It is darker 50,000 |- |- It is lighter || The city lights image is made up of more contrasting black and white shades than the gray texture in the baboon image. || || What value of pixel size and bits per pixel did you use? Size 2 Pixels/8 Bits per Pixel Would this image be suitable for a small part of a web page? Yes Would this image be suitable for a full screen display? No || Use the same settings for the sliders that were used in Question 11 || 524288 compared to 131072 bits. The image has better quality. How many bits are in this output image? 524288 || the image becomes more black and white || The original image is larger and more detailed. What characteristics of the image content of the canoe image require more bits to be reasonably reproduced in the output image? The people on the canoe and the trees in the background What makes false contouring more pronounced? less colors and flat areas ||
 * 1. || Start the lab 3.5.1 Resolution Trade-Offs Grayscale. An old world monkey should appear. ||
 * 2. || Set the pixel size slider to 1 and the bits per pixel slider to 8.
 * 3. || Set the pixel size to 2.
 * || Q1- How many bits are now used in the output image?
 * || Q2- What parts of the output image look the same as the original image?
 * 4. || Return the pixel size to 1.
 * || Q3-How many bits per pixel are used?
 * || Q4-What loss of image quality do you observe?
 * 5. || Set the pixel size to 2 and set the bits per pixel to 8.
 * || Q5- How many bits per pixel are you using?
 * || Q6-For the following bit budgets, find the choices for each slider that will give you the best image quality.
 * || Q7- In this lab we are downsampling first and then we are reducing the number of bits per pixel in the downsampled image.
 * 6. || Now we will look at a different image. Load cityLights256x256 ||
 * 7. || Compare the output image with a pixel size of 2 and 8 bits per pixel to the output image with a pixel size of 1 and 2 bits per pixel
 * || Q8-Which image has the better image quality?
 * || Q9-For the following bit budgets, find the choices for each slider that will give you the best image quality.
 * || Q10- Why would you spend your bit budget differently on the cityLights image than on the baboon image?
 * 8. || Change the input image to the girlOnWater256x256.bmp image ||
 * 9. || Explore the effects of changing the pixel size and the number of bit per pixel.
 * || Q11: Find the best settings for an image with less than 250,000 bits.
 * 10. || Change the input image to the canoeBW512x512.bmp image.
 * || Q12- How does this output image compare to the previous output image in quality?
 * || Q13- What happens if you try to reduce this image to less than 25,000 bits?
 * || Q14-Why is the canoe image with less than 25,000 bits so much poorer in quality that the girlOnWater image?
 * 11. || Stop the lab. Close the lab ||