In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests. Digital images and videos are everywhere these days – in thousands of scientific (e.g., astronomical, bio-medical), consumer, industrial, and artistic applications. Moreover they come in a wide range of the electromagnetic spectrum - from visible light and infrared to gamma rays and beyond. The ability to process image and video signals is therefore an incredibly important skill to master for engineering/science students, software developers, and practicing scientists. Digital image and video processing continues to enable the multimedia technology revolution we are experiencing today. Some important examples of image and video processing include the removal of degradations images suffer during acquisition (e.g., removing blur from a picture of a fast moving car), and the compression and transmission of images and videos (if you watch videos online, or share photos via a social media website, you use this everyday!), for economical storage and efficient transmission. This course will cover the fundamentals of image and video processing. We will provide a mathematical framework to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. In this class not only will you learn the theory behind fundamental processing tasks including image/video enhancement, recovery, and compression - but you will also learn how to perform these key processing tasks in practice using state-of-the-art techniques and tools. We will introduce and use a wide variety of such tools – from optimization toolboxes to statistical techniques. Emphasis on the special role sparsity plays in modern image and video processing will also be given. In all cases, example images and videos pertaining to specific application domains will be utilized.
JPEG
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Из курса от партнера Northwestern University
Fundamentals of Digital Image and Video Processing
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Из урока
Image Compression
In this module we cover fundamental approaches towards lossy image compression. Topics include: scalar and vector quantization, differential pulse-code modulation, fractal image compression, transform coding, JPEG, and subband image compression.
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Aggelos K. KatsaggelosJoseph Cummings Professor
Department of Electrical Engineering and Computer Science
Welcome back.
In this segment, we will look into some of the details of JPEG.
JPEG has been a very successful standard established over 20 years ago in 1991.
It has proliferated widely.
It can be found, for example, in almost every digital camera.
The acquired image is JPEG by default by the, one of the camera electronics.
And the high end cameras allow access to raw data.
JPEG like any compression scheme consists of three basic building blocks.
The first one performing redundancy reduction.
The second one performing quantization, and the third one, entropy encoding.
This idea is used for transforming each and
every eight by eight block of the image.
Quantization is performed by using uniform quantizers.
WIth different step sizes based on the frequency location of the coefficient.
So in general, fine uniform quantizers are used for
the low frequencies, and coarse quantizers for the high frequencies.
[BLANK_AUDIO].
The part which is terribly innovative in
JPEG is the application of entropy encoding.
The DC and AC coefficients are encoded differently.
Differential encoding is utilized for the DC coefficients by finding the difference
of the DC in the current block from that in the neighboring block.
For the entropy encoding of the AC coefficients,
the block is first vectorized by zig-zag scan.
And then, since, after quantization we have a
number of zero coefficients, the run of the
zeros preceding the value to be encoded is
encoded, followed by the value of the coefficient.
It is really a great demonstration of how
the source samples can be modified before entropy encoding.
So, let us proceed with the coverage of this successful standard.
In this segment, we will discuss the JPEG compression standard.
It stands for Joint Photographic Experts Group.
So JPEG is a working group which was formed in 86 by ISO and CCITT.
It became a international standard, JPEG, in 1991, more than 20 years ago.
It covers compression ratios 10 to 50, or resulting bits per pixel 0.5 to 2.
The objective of JPEG is the digital compression and
coding of continuous-tone still images, both grayscale and color.
It's clearly a highly proliferated standard.
It can be found, for example, in any digital camera.
The JPEG encoding actually takes place on the camera, and it's
probably an option of not JPEG-ing the image, only for high end cameras.
We saw that each and every compression scheme has three building blocks.
So, it's a transform based, approach.
But we discussed in the previous segment that the transformation used is the DCT.
We see how quantization is done in JPEG.
And, more importantly, we'll see the inventive
ways that are applied towards entropy encoding.
The image to be encoded is first divided into 8 by 8 blocks.
The blocks are zero-shifted, so they range from minus
128 to 127, for an 8-bit per pixel image.
Then the discrete cosine transform is taken of each and every block.
So here are the DCT coefficients.
The zero zero coefficient is referred to as the DC coefficient, while the rest are
referred to as AC coefficients from direct current and alternating current.
So, in this direction, the horizontal frequencies increase,
while in this direction the vertical frequencies increase.
After the DCT coefficients are quantized, they're zig-zag scanned.
The reason for that is to convert the two dimensional 8
by 8 array into an one dimensional 64 by 1 vector.
And the reason for that is that we want to generate
long runs of zeros that will be utilized during entropy coding.
So the idea of zig-zag scan is to visit the, visit
the coefficients along this path here, in the orbit indicated by this.
So the DC coefficient goes first, then this coefficient is the second one.
The third, fourth, fifth, sixth, and so on.
Entropy encoding in JPEG is performed in a very inventive, I would even say,
ingenious way, and provides a lot of
the power, the compression power for the standard.
The DC coefficients are encoded differently from the AC coefficients.
When comes to the DC coefficients, they're encoded
differentially from the DC coefficient in the neighboring block.
The main idea clearly is that neighboring blocks with have similar DC
coefficients and therefore the difference is going to be a small number.
This difference is encoded by the size first.
So there is a table here that encodes the different size is shown here.
By codes of different lengths, and here are the actual code words.
These are Huffman codes that are built on, on the value sizes.
So, for size 2, we use a 3 length code, and this is the specific code.
After that, the amplitude itself is sent.
So, if we look at the second table.
Size 2 encodes this for different amplitudes.
So this is the code for 2, the code for 3, and the code for minus 2, and minus 3.
When it comes to the encoding of the AC coefficients,
the first zig-zag scan as was explained in the previous slide.
And then each non-zero coefficient is first called in as Run followed by Size.
So there is yet another table.
Table three, that provides the Huffman codes for
this combination of Runs of zeroes followed by Size.
So, here's a Run of 0, 0's followed by size 2, the
length of this code is 2, and this is the actual code word.
Then, based on this Size here, part of this code word
will go back to table two and encode the amplitude value.
So, here is another great example that when we perform Huffman encoding, we don't
have to utilize the original symbols of
the source, the, these are the coefficients themselves.
But we invent new symbols for the DC again coefficient.
It's differentially encoded, and then we need the
two code words of Size followed by Amplitude.
While for the AC coefficients, after the zigzag scanning, we're looking
for runs of zeros, followed by the size, followed by the Amplitude.
Let us now walk through a JPEG encoding example.
Here is the image, we'd like to JPEG.
So we divide it into blocks, and to look specifically at this block over here.
And these are the values in this block.
The DCT of this block is computed, and here are the actual DCT values.
The DCT coefficients are quantized.
The way quantization is done in JPEG is shown here.
So, there are quantization tables like the one shown here.
What these numbers indicate is the step of a
uniform quantizer that is applied to this particular frequency.
So, we see by enlarge that fine quantization is applied
to lower frequencies, smaller quantizer step sizes.
While coarse quantization is applied to the high-frequency coefficients.
So then, the way quantization is done, is to find the ratio between the
coefficient and the corresponding entry in this
quantizer table, and perform the round operation.
So for example, if we look at this coefficient, the step
size of the uniform quantizer corresponding
to that figure's location is 51.
So 57 divided by 51, taking the round operation will result in a 1.
Similarly, if we quantize this coefficient, minus 29 is
divided by 103 and the quantized value is 0.
So we see that again, this table has spectral shape,
a lot of work has gone into designing such tables.
Quantization tables by also taking into
account properties of the human visual system.
So looking at the quantized, values what we see
is that there are now a good number of 0's.
And this is the motivation that we want to zigzag scan
these coefficients, and then encode them by looking at runs of zeros.
The other comment I would like to make here
is that what's sent to the decoder are these values.
And therefore during the construction, this value needs to be multiplied by the
corresponding step size to give us
the reconstructed value of the DCT coefficient.
After quantization, the DCT coefficients are vectorized or
zigzag scanned to form this one dimensional array.
Then this one dimensional vector will be input to the Huffman encoding step.
The DC coefficient is entropy encoded first.
So, the coefficient is 90, then this operation is performed due
to the fact that the mean was subtracted by, from the block.
And therefore the value of this DC coefficient is minus 38.
If we assume that the value of the previous DC coefficient was minus 46.
The difference is going to give us the value of 8.
So we need to encode this difference of 8.
We look at table T1 and see that 8 is here in
Size 4 and the Codeword for Size 4 is 101.
Then, we need to encode the actual value of 8, the amplitude.
So, if we look at table two, eight
is represented by this codeword, and therefore this is
the code, the codeword that would be sent to
the decoder to represent the differentially encoded DC coefficient.
Regarding the encoding of the AC coefficient, let us
assume we want to encode this coefficient of value 24.
If this was before quantization, first it would be quantized.
So the Quantizer is 12.
Therefore, the value is 2.
We look at the runs of zeros, and there are no preceding zeros.
Therefore the Size is 2, because 24 divided by 2 is equal to 2.
So we go to table T3 and look for Run, Size, Codeword.
So Run 0, Size 2 this is the actual codeword from table T3.
Then for Size 2, we go to table T2, and you see that the codeword for 2 is 10,
therefore, this particular value 24 is represented by the codeword 01, 10.
After entropy decoding and inverse quantization, the inverse-DCT is obtained.
So for this particular block, here are the values of
the inverse DCT that brings us back to the spatial domain.
Then the image is assembled from these 8 by 8 blocks, so this particular
8 by 8 block is placed back to its location in the image.
And for this particular block the compression error is shown here, that we
hear the exact values and here's a compression error for the whole image.
As expected the error is concentrated at the edges, at the areas of high spatial
activity, because if you think of a whole
process the high frequencies that are coarsely quantized.
And therefore thrown away this information at high frequencies and
therefore the arrow is going to have this high frequency appearance.
Here are some general observations regarding the performance of JPEG.
The performance is a function of the special resolution of the image,
so when you consider here a small format CGA 320 by 240.
Then, compression ratios at half a bit per pixel produce poor-quality results.
But if the resolution increases to VGA, the quality becomes fair.
And for Super VGA the performance becomes good.
Similar observations can be made for the other bit rates 2-bits per pixel.
Good, excellent, excellent.
This is actually a three circumstance that people could play at times, they
would propose a compression algorithm tested at higher resolution images.
Such high resolution images have considerably
more correlated data, and therefore more
can be gained during compression, or we can compress the data more.
The perceptually lossless results are obtained at bit rates
one and a half to 2 bits per pixel.
We show here some results using JPEG at various bit rates.
So, cameraman at one and a half bits per pixel.
We see no distortion, it seems very, very close to the original image.
At one bit per pixel, the quality is still very good.
You start seeing some artifacts around the edges, here.
At half a bit per pixel, then, these ringing type of artifacts are quite
pronounced, and also you see blocking artifacts
in the smooth regions of the image.
And if we go further down to 0.2 bits per pixel, then you see plenty of this
contouring, the artificial contouring and the edges are all distorted.
However it's still impressive that with only 0.2 bits per pixel on
the average we see, we have some good idea of what is in the picture.
There's quite a bit of work in the literature, actually, as
I mentioned when we covered recovery, in removing these blotting arifacts.
There's a recovery problem.
And for the post-processing step after JPEG or any
compression approach for that matter, we tried to remove
some of the artifacts that were introduced due to
compression and therefore improve the visual quality of the image.
For this example, a standard quantization table
was used like the one I showed earlier.
One could modify that table and use a flat table for example.
Or use a table that would perform fine quantization
at high frequencies and coarse quantization at low frequencies.
You can experiment with all these things
using, for example, the VCDemo software package.
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