How a Picture Becomes Smaller
Written by Studio AM.
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A digital picture can contain millions of color values. Saving every value exactly may create a large file, so compression uses patterns to represent the image with less data.
Lossless compression finds repetition without throwing information away. If a row contains many identical pixels, the file can record the color once and describe how often it repeats. When opened, the original values can be rebuilt exactly.
Lossy compression goes further. It removes some detail that a viewer may be less likely to notice, such as tiny color changes in a busy area. Stronger compression usually makes a smaller file, but it can also produce blocky edges or blurred texture. Once discarded, those details cannot be recovered merely by reopening the file.
Neither method is always best. A medical scan or an image meant for later editing may require exact detail. A casual photograph on a slow connection may benefit from a much smaller file. The useful choice depends on what the image must preserve and how it will travel.
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Question 1 of 4
What is the main idea of the passage?
The answer is D: Image compression reduces file size by encoding patterns or removing detail, with the best method depending on purpose.
The passage compares two methods and closes by matching their different tradeoffs to different uses.
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Question 2 of 4
Why might a medical scan use lossless compression?
The answer is A: Its exact recorded details may need to remain available.
The final paragraph associates scans with exact detail, and lossless compression preserves original values.
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Question 3 of 4
What does “discarded” mean in the third paragraph?
The answer is B: removed and not kept
The sentence says those details cannot be recovered, showing that they were removed.
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Question 4 of 4
What visible problem can strong lossy compression create?
The answer is C: blocky edges or blurred texture
The third paragraph directly lists blocky edges and blurred texture as possible results.
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