In this course, you will learn the science behind how digital images and video are made, altered, stored, and used. We will look at the vast world of digital imaging, from how computers and digital cameras form images to how digital special effects are used in Hollywood movies to how the Mars Rover was able to send photographs across millions of miles of space. The course starts by looking at how the human visual system works and then teaches you about the engineering, mathematics, and computer science that makes digital images work. You will learn the basic algorithms used for adjusting images, explore JPEG and MPEG standards for encoding and compressing video images, and go on to learn about image segmentation, noise removal and filtering. Finally, we will end with image processing techniques used in medicine. This course consists of 7 basic modules and 2 bonus (non-graded) modules. There are optional MATLAB exercises; learners will have access to MATLAB Online for the course duration. Each module is independent, so you can follow your interests.
6 - JPEG_LS and MPEG - Duration 19:32 - Optional break at 13:45
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Habilidades que você aprenderá
Image Processing, Image Compression, Image Restoration, Image Segmentation
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4.8 (606 classificações)
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MK
Oct 14, 2018
A very nice covering of subject from basics to advance level of image processing. A good insight into theory and implementation of the Image processing algorithms.
KS
Oct 14, 2017
Wonderful course with a lot of content to learn which helps in developing a better conceptual background for various Image processing applications.
Na lição
Image and video compression
JPEG and MPEG are the most successful algorithms in the area, widely used by everybody in a daily basis, and the goal of this unit is to understand how they work. Also to understand why these techniques are important and why they are enabling technologies. Also will describe what is done in the Mars expedition.
Ministrado por
Guillermo Sapiro
Professor
While JPEG is the most important image compression algorithm, and as we
discussed before, probably the most important image processing algorithm
overall, there are other techniques for image and video compression which are
very important. And I want to describe the basic
underlying, the concepts behind two of them.
One is JPEG-LS, and the other is MPEG. JPEG-LS basically permits us to compress
lossless, basically without any loss of information, preserving exactly the pixel
value at every single pixel in the image. An MPEG is the one that we use to
compress video, and you probably have MPEG in your mobile
phones as well, and you have seen videos on the internet
that are compressed with MPEG. So let's describe the underlying concept
between these two very important image and video compressing, image and video
procession techniques as what? And we're going to start with JPEG-LS
concepts, basically. And the idea behind that is what's called
predictive lossless compression. So for comparison, I left here the basic
diagram that we have seen so many times already for JPEG-LS,
while this is the basic diagram for predictive coding.
And the basic idea of predictive coding is very, very simple.
It's even simpler than the idea of JPEG. And let's just illustrate that with an
example. We're just draw a small portion of an
image. And, in predictive coding, the first
thing that we have to define, is an order.
We're going to compress one pixel after another.
And, let's say that we define what's called, a roster order, that we basically
go from left to right, and we'll encode the first row, then we code, the second
row, and we keep going. So let's say that we have encoded
everything and now we need to encode this pixel.
The idea of predictive coding is to guess, to predict the value of this pixel
based only on the past. We cannot predict it based on the future
because the decoder doesn't know the future.
That the caller has only received all this pictures.
So we could, for example say, okay, I'm going to guess, I'm going to predict that
the value of this pixel is identical to the value of the previous pixel.
And then what we do is, after we do the prediction, we basically compute the
error, between my prediction and the actual value.
And what we encode, let's say with Huffman coding here, is the error.
And the decoder basically is going to emulate what the encoder is doing.
It's basically going to say, okay, I'm going to do the same prediction.
That you did. I know the past, exactly as you know it,
and I'm going to predict that this pixel, with this value, so the color and the
color talk with each other. They are using exactly the same
prediction strategy. So, I predicted this one, and you gave me
the error. I basically add to my prediction the
error, and I have this pixel value. And then I continue to the next pixel.
I do exactly the same. Process.
And the basic idea is that if you are very good at predicting this error is
going to be very close to zero, all the time.
It's not going to have any possible value.
It's going to have values around zero, and that's exactly if you remember what
encoders like Huffman coding likes. To have values that appear a lot, so we
can give a very short code to them, and some values, once in a while you're
going to make a big mistake in your prediction.
Don't worry, because you're going to encode error,
and basically when, by encoding the error, you're going to
have lossless compression. So once in a while you make a big
mistake. Then Huffman will use a larger code for
that mistake, but because that happens once and awhile, it won't be so
problematic, and your going to be able to still compress.
So, a lot of the art in predictive lossless compression is in designing, in
designing your predictor. Such that the error is as small as
possible all the time. Hopefully, zero.
If you're great in predicting, it's going to be always zero.
And it's going to be able to compress a lot.
But at least it's going to be very, very close to zero if you're doing smart
predictors. And then the decoder is going to just do
a very, very simple decoding. So know that overall this structure of a
predictive lossless compression technique is a bit simpler than the one of JPEG.
Ofcourse it's not going to be able to achieve as much compression.
Base is going to be lossless which is very important.
So lets just illustrate a couple of possible.
Predictors. So we have our image.
An. We're going to, basically, drop the
liters, again, let us see that all these have been encoded.
And lets just consider now, that what I want to do is predict, this pixel.
So, one thing I can do, is predict it based on the past.
Just on this pixel. So I can say that I predict this to be F
of X minus one Y, and then, if I'm the encoder, I'm going to take actual F of
XY. I'm going to substract, so my error at
this pixel will be F. Of X minus one, Y minus F.
Xy. And this what I'm going to basically save
through half my coding. And then once again the decoder is
going to emulate my predictor, it's going to say oh you predicted this, you
gave me an error, I add the error to my prediction, I get this pixel value.
Very, very simple operation, in particular for the decoder, it's a, it's
a really, really simple operation. So, one possible predictor is just to use
the past. Another is basically to use the two
previous pixels. Let's say an average of the three, two
previous pixels. Some other predictors, like, for example
JPEG-LS, we'll actually consider a bit more.
We'll basically try to use, be a bit smarter, and maybe use all this
region. Say, okay,
why only to use the one on the left? Maybe I can also use the one on top of
you, and the one basically diagonal. And, you can do normal combinations of
them. As I say, you could, if you were to use
these two, you could basically average them.
You can also average these three. Actually JPEG-LS does a bit more
elaborated technique. So JPEG-LS is the standard for lossless
image compression. It's actually the algorithm that is in
the Mars rovers. One of the algorithms that, that are in
the Mars rovers for image compression is JPEG-LS and it has.
a bit smarter. It tries to find basically where edges
are in the image. But it's also are very simple, remember
compression has to be simple in order to become a standard, so that everybody can
implement them and they can run very fast.
And we have seen that concept appear already in JPEG.
It also appears in JPEG-LS, and it will appear as well in MPEG.
So you could use this region. Basically you could use as I said the
whole past. You could basically say I'm going to
write to use only these pixels. maybe if I have encoded already, if I'm
in the middle of a very large image, I could basically use my whole pass,
past data, all the way to here.
The problem is that if you do that, you're going to have to remember a lot of
pixels, it's going to take a lot of memory, if you just used everything in
the past, it's just too much. So JPEG-LS has most modern predictive
lossless compression techniques. This is, may be the previous role, at
most two roles before, but not much more than that.
Otherwise, SSA becomes too complex and too much memory.
So, that's basically the idea of predictive coding.
Now let's see how effective this is with just a very simple image.
So this is a nice image from earth, taken from outer space.
And this is a distribution. Remember we talked about histograms.
This is the distribution of pixel values. Okay?
So you see Huffman might like this because there are some concentration of
pixel values around 150, but there is still a very, very wide distribution.
Now if we do predictive coding with one of the predictors I just mentioned.
So we go roster like this and at every point we basically predict the value
based on some very small neighborhood around that value.
Then the error is distributed as here. So we predict, we compute the error as I've
shown in the previous slide, and we plot the distribution of error, the histogram.
But look how nice it is, it's a smart predictor and it's a relatively nice
image. And then we basically get a strong
concentration around zero, and you can observe here a plot of the prediction
error and as expected when then are big changes,
your prediction doesn't know that a change is about to happen. So that's
where you make most of the mistakes. In regions that are relatively uniform,
you don't make a lot of mistakes, so that's why you get a lot of zeros.
And okay, once in a while, there's a big change you make a mistake and Huffman is
going to take more bits to represent that prediction error, but that happens once
in a while. So most of your errors of the prediction
are very small, and that's really, really good.
So that's basically the underlying idea of JPEG-LS, or any predictive lossless
encoder or compression. Now, you can add quantization as well.
In a predictive coding, or in a predictive image compression technique.
So basically you quantize the error. The same way that we saw how to quantize
transform coefficients, you quantize the error.
And with that, you can achieve more compression.
You can actually have control on how much error.
So if you are allowing +-plus minus 1 pixel value of error, of quantization in
your error, then you know that your reconstructed
image will differ, will differ from the original image only
by +-plus minus 1 grey value. So you can have a lot of control.
Because you're not working the transform domain,
you're working the image domain. And that's also part of JPEG-LS,
that you can add quantization to your errors.
basically, the decoder will receive a quantized error, and will reproduce a
pixel that has a small variation compared to the original pixel.
One very important thing to have in mind is that when you introduce quantization,
you have to do your prediction based on the quantized error. So let's just do the
same drawing that we had before. If we are going to predict this pixel
based on this one, then remember,
the decoder is going to only have the quantized version of that after it has
reconstructed the quantized error. So at the encoder, you have to also
emulate what the decoder is doing and consider only the quantized version of
your pixel. If you were to predict based on the
original version, then you're predicting based on something that the decoder
doesn't have. And that's why it's the quantized value
that enters the area of the predictor in this drawing.
That's just something to have in mind in case you're designing basically a
predictive loss, a predictive lossless and lossy image compression.
If you want to have quantization, remember, you cannot use anything in
image compression that the decoder doesn't have.
As an encoder, you have only, you are only allowed to use things that
the decoder is going to see. Otherwise, this basically is not a
symmetric system, and it won't work. So, that's just the only thing to have in
mind. And as I say JPEG-LS includes a
quantization. So this is the basic underlying idea of
lossless, predictive coding, or lossy, but with
control, loss. Now, that's not the only way that we can
do prediction. And basically MPEG, which is the standard
for video compression, is also based on prediction.
But now, I have actually multiple frames. So remember in video, we have let's say
30 frames a second. So in order to predict, for example,
this frame, I can use past frames to predict it.
And, the basic idea is very simple. If I'm filming a scene that is completely
static, then every frame will be identical to the previous frame.
And MPEG is trying to take advantage of that.
It's trying to say, okay, let's just look at the region in an image
and let's see if that region, this basic constant, and you can do it in
multiple fashions, and we're not going to discuss all the
details behind MPEG. But for example, you can actually say
okay, I'm going to see if this region here; if there is a similar region around
it, it doesn't have to be exactly at the same place, it can be around it.
It can basically look for similar regions in previous frames, maybe in only in one
frame, maybe only in three frames before me, it's going to look for similar
regions and it's going to basically also try to encode only the error.
So it's going to do prediction in time. Okay?
And it's a very, very simple idea, and that works extremely well.
We know that MPEG actually compresses a lot,
thanks to the fact that for example, this is a good example. The only thing that is
moving in this video is me. The whole background is constant.
So, if you pick blocks in the background, you're going to find in previous frames
basically identical blocks and then the prediction error will be zero and that's
great for the encoder. So here is an example with basically we
have the same image that we had before but basically we have two frames of that
image one and two and we do a very simple prediction here, which basically we say,
okay, the second image is identical to the first image.
If there is not a lot of motion, we see that prediction is really, really good.
Most of the time it's zero, as we have seen before for the special predictor.
This is a temporary predictor. Once again, every pixel here is predicted
as to be identical to the pixel in the same position in the previous image.
We can be smarter. We can look for that pixel in a region
around it, and then we are going to encode both the value and the offset So,
did I find it to the left, or did I find it,
eh, eh, downwards. But here it was the simplest possible
thing. Just basically exactly in the same
position. And this is the difference, and this is
the distribution of the error. So basically, if I know this frame.
I'm going to be able to reproduce this frame very, very efficiently by copying
the previous frame, and adding the, this image, which is the error image.
Copying the previous frame is free, because I already have it.
The decoder array has it. And compressing this, because it's so
concentrated in zero, it's really, really very efficient with techniques with
Huffman coding. I add these two images, I get this one,
and then I continue to the next frame. And of course, if there is a lot of
motion, the error image won't be as simple, but hopefully it will be simple
enough that Huffman can take advantages of it.
And this is the basic block diagram of MPEG, or video compression techniques.
It's very similar to what we have seen for JPEG.
It basically has DCT, quantization, variable length coding like half man.
It only has this additional component here, which is basically encoding the
prediction in time. So it's the one that takes care of doing
this motion estimation and saying, okay, I want to take this block from a region
in the past. But the basic structure is the samic
structure, plus this that takes into account that
I'm not compressing just one image, and basically compressing multiple
frames, and many of them are very similar.
So, once again, predictive coding. Here actually is a combination between
the techniques that we have seen for JPEG,
and the techniques that we have seen for predictive coding.
Both together produce MPEG. With this, we basically have concluded
the basic underlying concepts of image and video compression.
So we have discussed JPEG, we have discussed JPEG-LS, and we have discussed
MPEG. We did transform, we did predictive
coding, we did quantization, we did basically Huffman coding.
The whole building blocks of compression, which is what we basically use daily in
image and video processing. In the next video, which is going to be a
bonus video, if you have time, I recommend you to watch it.
I'm going to describe very briefly what's called run length coding, just to
complete the picture. But, if you don't have time, you can just
skip that video, and next week go into the next unit.
Thank you very much.
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