CODEWITHSUNDEEP
matlabimage-processingrgbhsv
Hossein Rashidi
Hossein Rashidi

Reputation: 78

Block artifact in converting RGB 2 HSV

I would like to convert an image from RGB space to HSV in MATLAB and use the Hue.

However, when I use 'rgb2hsv' or some other codes that I found in the internet the Hue component has block artifacts. An example of the original image and the block artifact version are shown below.

Original

blah

Hue

blah

Upvotes: 3

Views: 1861

Answers (1)

rayryeng
rayryeng

Reputation: 104565

I was able to reproduce your error. For those of you who are reading and want to reproduce this image on your own end, you can do this:

im = imread('https://i.sstatic.net/Lw8rj.jpg');
im2 = rgb2hsv(im);
imshow(im2(:,:,1));

This code will produce the output image that the OP has shown us.

You are directly using the Hue and showing the result. You should note that Hue does not have the same interpretation as grayscale intensity as per the RGB colour space.

You should probably refer to the definition of the Hue. The Hue basically refers to how humans perceive the colour to be, or the dominant colour that is interpreted by the human visual system. This is the angle that is made along the circular opening in the HSV cone. The RGB colour space can be represented as all of its colours being confined into a cube. It is a 3D space where each axis denotes the amount of each primary colour (red, green, blue) that contributes to the colour pixel in question. Converting a pixel into HSV, also known as Hue-Saturation-Value, converts the RGB colour space into a cone. The cone can be parameterized by the distance from the origin of the cone and moving upwards (value), the distance from the centre of the cone moving outwards (saturation), and the angle around the circular opening of the cone (hue).

This is what the HSV cone looks like:

blah

Source: Wikipedia

The mapping between the angle of the Hue to the dominant / perceived colour is shown below:

blah

Source: Wikipedia

As you can see, each angle denotes what the dominant colour would be. In MATLAB, this is scaled between [0,1]. As such, you are not visualizing the Hue properly. You are using the Hue channel to directly display this result as a grayscale image.

However, if you do a scan of the values within this image, and multiply each result by 360, then refer to the Hue colour table that I have shown above, this will give you a representation of what the dominant colours at these pixel locations would be.

The moral of this story is that you can't simply use the Hue and visualize that result. Converting to HSV can certainly be used as a pre-processing step, but you should do some more processing in this domain before anything fruitful is to happen. Looking at it directly as an image is pretty useless, as you have seen in your output image. What you can do is use a colour map that derives a relationship between hue and colour like in the Hue lookup map that I showed you, and you can then colourize your image but that's really only used as an observational tool.

Edit: July 23, 2014

As a bonus, what we can do is display the Hue as an initial grayscale image, then apply an appropriate colour map to the image so we can actually visualize what each dominant colour at each location looks like. Fortunately, there is a built-in HSV colour map that is pretty much the same as the colour lookup map that I showed above. All you would have to do is do colormap hsv right after you show the Hue channel. We can show the original image and this colourized image side-by-side by doing:

im = imread('https://i.sstatic.net/Lw8rj.jpg');
im2 = rgb2hsv(im);
subplot(1,2,1);
imshow(im); title('Original Image');
subplot(1,2,2);
imshow(im2(:,:,1)); title('Hue channel - Colour coded');
colormap hsv;

This is what the figure looks like:

enter image description here

The figure may be a bit confusing. It is labelling the sky as being blue as the dominant colour. Although this is confusing, this makes actual sense. On a clear day, the sky is blue, but the reason why the sky appears gray in this photo is probably due to the contributions in saturation and value. Saturation refers to how "pure" the colour is. As an example, true red (RGB = [255,0,0]), means that the saturation is 100%. Value refers to the intensity of the colour. Basically, it refers to how dark or how light the colour is. As such, the saturation and value would most likely play a part here which would make the colour appear gray. The few bits of colour that we see in the image is what we expect how we perceive the colours to be. For example, the red along the side of the jet carrier is perceived as red, and the green helmet is perceived to be green. The lower body of the jet carrier is (apparently) perceived to be red as well. This I can't really explain to you, but the saturation and value are contributing to the mix so that the overall output colour is about a gray or so.

The blockiness that you see in the image is most likely due to JPEG quantization. JPEG works great in that we don't perceive any discontinuities in smooth regions of the image, but the way the image is encoded is that it reconstructs it this way... in a method that will greatly reduce the size it takes to save the image, but allow it to be as visually appealing as if you were to look at the RAW image.

The moral of the story here is that you can certainly use Hue as part of your processing chain, but it is not the entire picture. You will probably need to use saturation or value (or even both) to help you discern between the colours.

Upvotes: 4

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