how can convert 256x256 gray image to 6 grayscale?

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elTurco 2023-4-11

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编辑： DGM 2023-4-11

Hi everyone.

I want to convert a gray 256x256 pixel image to 6 grayscale.

And i want to calculate the entropy of new image. Could anyone help me??

Have a good day.

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Antoni Garcia-Herreros 2023-4-11

What do you mean by 6 grayscale? 6 bit image? Divide your grayscale image in 6 regions?

elTurco 2023-4-11

I mean convert 256 gray level to 6 gray level.

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Answer 1

Antoni Garcia-Herreros 2023-4-11

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编辑：Antoni Garcia-Herreros 2023-4-11

在 MATLAB Online 中打开

Hello Emirhan,

You could try something like this:

Use the command entropy

%Create the 256 grayscale image for the example

I=imread('peppers.png');

IBW=rgb2gray(I);

IG256=im2double(IBW)*255; % This would be your image

subplot(1,2,1)

imshow(IG256,[])

title('256 Grayscale')

val=255/5;

IG6=round(IG256/val); % Assign a value from 0 to 5

subplot(1,2,2)

imshow(IG6,[]);title('6 Grayscale')

Entropy=entropy(IG6);

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DGM 2023-4-11

编辑：DGM 2023-4-11

在 MATLAB Online 中打开

If you run the code with a different image, you may notice that the calculated entropy is zero. That's because of two things. First, the image is not correctly scaled for its class. Second, because the image is low-contrast and has no content near black, the uniform quantization means that all pixels are in the same gray level due to truncation (again, because the image is improperly-scaled). As far as entropy() is concerned, it's being given a featureless white image.

You can simplify the code and fix the error like so. (I omitted everything but the core operations)

I = imread('pout.tif'); % this image is low-contrast
IBW = im2gray(I); % convert if RGB
N = 6; % number of gray levels
IG6 = round(im2double(IBW)*(N-1))/(N-1); % correctly-scaled for its class
Entropy = entropy(IG6)
Entropy = 0.9296

This result should be the same as using gray2ind()

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Answer 2

DGM 2023-4-11

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在 MATLAB Online 中打开

You haven't mentioned at all how you want to quantize the image. I imagine that the entropy depends on that.

% assuming input is gray

inpict = imread('pout.tif'); % single-channel, uint8

nlevels = 6;

% uniform quantized using gray2ind()

% mapping corresponds to nominal range, not image extrema

[graypict1 map1] = gray2ind(inpict,nlevels); % quantize

graypict1 = im2uint8(ind2gray(graypict1,map1)); % convert to an intensity image

imshow(graypict1)

% minimum variance quantization using rgb2ind()

% mapping corresponds to image extrema

expanded = repmat(inpict,[1 1 3]); % expand the image

[graypict2 map2] = rgb2ind(expanded,nlevels,'nodither'); % quantize

graypict2 = im2uint8(ind2gray(graypict2,map2)); % convert to an intensity image

imshow(graypict2)

% colormap approximation (map to black & white)

% approximately the same as uniform quant

% since the map is uniform and full-range

map3 = gray(nlevels); % a full-range color table

graypict3 = rgb2ind(expanded,map3,'nodither'); % quantize

graypict3 = im2uint8(ind2gray(graypict3,map3)); % convert to an intensity image

imshow(graypict3)

% colormap approximation (map to extrema)

% in this case the map is still uniform, but only spans the image extrema

map4 = rescale(map3,im2double(min(inpict(:))),im2double(max(inpict(:))));

graypict4 = rgb2ind(expanded,map4,'nodither'); % quantize

graypict4 = im2uint8(ind2gray(graypict4,map4)); % convert to an intensity image

imshow(graypict4)

Now do the same things, but with FS error-diffusion dithering

% minimum variance quantization

expanded = repmat(inpict,[1 1 3]); % expand the image

[graypict5 map5] = rgb2ind(expanded,nlevels,'dither'); % quantize

graypict5 = im2uint8(ind2gray(graypict5,map5)); % convert to an intensity image

imshow(graypict5)

% colormap approximation (map to black & white)

graypict6 = rgb2ind(expanded,map3,'dither'); % quantize

graypict6 = im2uint8(ind2gray(graypict6,map3)); % convert to an intensity image

imshow(graypict6)

% colormap approximation (map to extrema)

graypict7 = rgb2ind(expanded,map4,'dither'); % quantize

graypict7 = im2uint8(ind2gray(graypict7,map4)); % convert to an intensity image

imshow(graypict7)

% compare them

[entropy(graypict1)

entropy(graypict2)

entropy(graypict3)

entropy(graypict4)

entropy(graypict5)

entropy(graypict6)

entropy(graypict7)]

ans = 7×1

0.9296 2.2557 0.9250 1.8247 2.3047 1.3267 1.7909

So what's the entropy of the an arbitrarily quantized image? How long is a piece of string?

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