In MATLAB, a grey-scale image is stored a a matrix of numbers, all between 0 (black) and 255 (white). Since there are 256 possible values, 8 bits are needed or each pixel, in contrast to a normal integer, which is 64 bits.To save memory, grey-scale images are stored with a special data type, uint8 (unsigned 8-bit integer). In computer terminology, 8 bits = 1 byte. Even at one byte per pixel, a large image can consume a lot of memory. For example, a 1000x1000-pixel image takes 1 million bytes (which is a little less than 1 megabyte, since by convention 1 Mbyte = 2^20 bytes). One technique for reducing the size of an image is quantization - reducing the number of allowable levels from 256 to something smaller. Surprisingly, most images can be quantized by a large factor without losing much information. In this problem, you will quantize a grey-scale image to only 4 levels, so that it could (in principle) be stored as 2 bits per pixel. The template will read in an image file. Your job is to loop over all rows and columns of the image matrix, N, and set each pixel to 0, 1, 2, or 3 as follows: original pixel value new pixel value 0 - 64 0 65 - 128 1 129 - 192 2 193 - 255 3 Use imshow to display the original and modified images so that you can see the effect of the quantization.

Name: In MATLAB, a grey-scale image is stored a a matrix of numbers, all bet
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Description: In MATLAB, a grey-scale image is stored a a matrix of numbers, all between 0 (black) and 255 (white). Since there are 256 possible values, 8 bits are needed or each pixel, in contrast to a normal integer, which is 64 bits.To save memory, grey-scale i

C++ for Engineers and Scientists

4th Edition

ISBN:9781133187844

Author:Bronson, Gary J.

Publisher:Bronson, Gary J.

Chapter2: Problem Solving Using C++using

Section2.3: Data Types

Problem 9E: (Practice) Although the total number of bytes varies from computer to computer, memory sizes of...

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In MATLAB, a grey-scale image is stored a a matrix of numbers, all between 0 (black) and 255 (white). Since there are 256 possible values, 8 bits are needed or each pixel, in contrast to a normal integer, which is 64 bits.To save memory, grey-scale images are stored with a special data type, uint8 (unsigned 8-bit integer). In computer terminology, 8 bits = 1 byte. Even at one byte per pixel, a large image can consume a lot of memory. For example, a 1000x1000-pixel image takes 1 million bytes (which is a little less than 1 megabyte, since by convention 1 Mbyte = 2^20 bytes). One technique for reducing the size of an image is quantization - reducing the number of allowable levels from 256 to something smaller. Surprisingly, most images can be quantized by a large factor without losing much information. In this problem, you will quantize a grey-scale image to only 4 levels, so that it could (in principle) be stored as 2 bits per pixel. The template will read in an image file. Your job is to loop over all rows and columns of the image matrix, N, and set each pixel to 0, 1, 2, or 3 as follows: original pixel value new pixel value 0 - 64 0 65 - 128 1 129 - 192 2 193 - 255 3 Use imshow to display the original and modified images so that you can see the effect of the quantization.

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