So if that prediction is possible then it can be argued that why do have to

So if that prediction is possible then it can be

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point, I can predict what is the intensity of its neighboring point. So if that prediction is possible then it can be argued that why do have to store all those image points? Rather I store one point and the way it can be predicted, its neighborhood can be predicted, we just mention that prediction mechanism. Then the same information can be stored in a much lower space. You will find this second region. Here again in most of the regions you find that intensity is more or less uniform except certain regions like eye, like the hat boundary, like the hair and things like that. So these are the kind of things which are known as redundancy.
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(Refer Slide Time 19:13) So whenever we talk about an image, the image usually shows 3 kinds of redundancies. The first kind of redundancy is called a pixel redundancy which is just shown here. (Refer Slide Time 19:22) The second kind of redundancy is called a coding redundancy and the third kind of redundancy is called a psychovisual redundancy. So these are
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(Refer Slide Time 19:33) the 3 kind of redundancies which are present in an image So whenever we talk about an image (Refer Slide Time 19:42) the image contains two types of entities, the first one is the information content of the image and the second one is the redundancy and these are the three different kinds of redundancies. So what is done for image compression purposes, you process the image and try to remove the redundancy present in the image, retain only the information present in the image. So if
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we retain only the information, then obviously the same information can be stored using a much lower space. The applications of this are reduced storage as I have already mentioned (Refer Slide Time 20:20) if I want to store this image on a hard disk, or if I want to store a video sequence on a hard disk, (Refer Slide Time 20:27) then the same image or the same digital video can be stored in a much lower space. The second application is reduction in bandwidth.
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(Refer Slide Time 20:39) That is if I want to transmit this image over a communication channel or if I want to transmit the video (Refer Slide Time 20:45) over a communication channel then the same image or the same video will take much lower bandwidth of the communication channel. Now given all these applications, this again shows that
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(Refer Slide Time 21:02) what do we get after completion? So here we find that we have the first image which is the original image, the second one shows the same image but here it is compressed 55 times. So you find, if I compare the first image and second image, I find the visual quality of the two images are almost same, at least visually we cannot make much of difference. Whereas if you look at the third image which is compressed 156 times, now if you compare third image with the original image you will find that in the third image there are a number of blocked regions or blocking, these are called blocking artifacts which are clearly visible when you compare it with the original image. The reason is, as we said that the image contains information as well as redundancy. So if I remove the redundancy, maintain only the information then the
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  • Spring '15
  • Dr Mahmoud Khalil

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