在本门课程中，您将学习图像和视频处理的基本概念，以及如何用这些概念解决商业或科学中的实际问题。 数字图像和视频现已无处不在：在科技（例如航天、生物医学等领域）、消费、工业、艺术等领域都有应用；而且已经涵盖了电磁频谱中很大一部分：从可见光、红外线到伽马射线，甚至更远。因此，处理图像和视频信号的能力对于工科和理科学生、程序员、科学家来说已经成为了必须掌握的重要技能。数字图像和视频处理使我们正在经历的多媒体技术革命成为可能。几个重要的图像和视频处理的例子：图像劣化的去除（例如将快速移动的汽车的照片去模糊），为了节约存储和高校传输对图像和视频进行的压缩和变换（如果你观看网络视频，或者用社交网络分享图片，那么你就已经用到了这些技术）。 本课程将涵盖图像和视频处理的基础部分。我们会介绍描述和分析2D/3D图像和视频的数学框架，包括空间域、时空域、频域。在本课程中，您将学到基础图像和视频处理方法背后的理论，包括图像/视频增强、回复、压缩，您也将学到如何使用最新工具将这些方法用于实际的任务。从优化工具到统计方法，我们会介绍并使用很多这样的工具。我们也会介绍在现代图像和视频处理中扮演重要角色的“稀疏性”。在所有案例中，特定应用下的示例图像和视频都会被用到。
Run-Length Coding and Fax
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来自 Northwestern University 的课程
数字图像和视频处理基础
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从本节课中
Lossless Compression
In this module we introduce the problem of image and video compression with a focus on lossless compression. Topics include: elements of information theory, Huffman coding, run-length coding and fax, arithmetic coding, dictionary techniques, and predictive coding.
与讲师见面
Aggelos K. KatsaggelosJoseph Cummings Professor
Department of Electrical Engineering and Computer Science
In this segment we cover one of the earliest applications of Huffman encoding.
Which is the transition of scan printed material over telephone
lines which is referred to as facsimile or fax application.
The printed text to be submitted is scanned and converted into a binary image.
Since these 0s and 1s representing the
text are not randomly scattered over the image.
But instead, we have structure.
This structure is exploited.
The structure results in a number of consecutive 0s or 1s in a row,
so-called runs, to be encoded instead of the individual 0s and 1s.
This results in the so called run length encoding.
It is a demonstration of a theme that will appear
in various forms over these three weeks we cover compression.
Some of the ingenuity in an existing algorithm standards.
Comes from this reinvention of the symbols of the source.
The symbols do not have to be the original pixel values.
But innovative combinations of them.
As in rounding through or in triplets, which combines
various properties of the image, as we'll see next week.
A lot of compression power comes from this.
So let us proceed with the material for the assignment.
Facsimile encoding can be applied directly to the lossless compression of images.
For an eight-bit per pixel image, for example,.
The source alphabet contains 256 symbols from 0 to 255 intensities of an image.
In this case, compression ratios between one and two are achieved.
It can also be applied to the compression of text, although
we'll talk about dictionary techniques which are more suitable for that purpose.
One of the earliest applications of lossless compression
has been the compression of facsimile or fax.
In fax transmission a page is scanned and converted into a binary image.
A sequence of black or white pixels.
Then, this image is compressed, is sent over the network.
And, at the destination, is decompressed and printed, typically.
The requirements of how fast an A4 document must
be transmitted, have changed over the last two decades.
[UNKNOWN] Dimension of the A4 document.
And here is some of the recommendations of the pixel and line resolution.
There's then the [UNKNOWN] body CCITT.
The current
name is actually ITU-T.
Has issued a number of recommendations based
on the speed requirement at any given time.
The CCITT classifies the apparatus for fax transmission into four groups, G1 to G4.
So we're going to look at some of the techniques that
are used to encode this binary image that results during fax encoding.
We mentioned here some of the characteristics
of group three and group four fax standards.
They were establish for the compression of text documents and
therefore either tables, and charts, and half tone images involved.
And they don't work as well.
G3 is for the digital operation with analog modems.
And the very important concept here, that of run length
coding is used, both in one dimension and two dimension.
So we'll spend a few minutes after that discussing run, length and coding.
Then, based on these run lengths, of 0s and 1s static Huffman tables are utilized.
When 2D run length is used then, in addition
to the current line, the previous line is considered.
And in G3, we have a combination of 1D
and 2D run length coding to prevent error propagation.
G4 is an all digital for digital network such as ISDN
and in this case only a two dimensional run length is utilized.
So, we will look into run length and then show some static
Huffman tables that are utilized in these fax standards.
Run length coding is another straightforward concept.
If we look at the binary image
which results from this scanning over text document.
Then what we see is sequences or runs of white and black pixels.
So when a black symbol of, of text is scanned, then it generates a run of 0s.
So it scales Z a round of three 0s, and when the
white background, the white pages scan, then we see rounds of white pixels.
And here we see a round of nine white pixels.
So the very basic idea is that instead of encoding
0s and 1s, black and white pixels directly.
Instead we code the runs, how many black pixels I have in
a row, how many white pixels do we have in a row.
So this becomes now the symbols of the source.
And this is a concept we'll be encountering over, and over again.
When we talk here about compression.
So, we don't have to stick to a source that generates 0 and 1s,
but now we have this new source that emits runs of 0s and runs of 1s.
A two-state Markov model, shown here.
Gives rise to run length coding.
The states sw
and sb correspond to, like sw for example corresponds to the case
where the pixel that has just been encoded is a white pixel, and similarly for sb.
The transition probabilities and the probabilities of
being in its state, completely specify this model.
So, for fax images, these probabilities
are not uniform, but they're instead skewed.
Which means that, once a pixel takes a particular color, black or white.
Then it's highly likely that the following pixel will also be of the same color.
So therefore, instead of encoding each color separately, we encode the runs.
We can actually compute the entropy
of this two state Markov model as the
probability of being in state y times the
entropy of the y state, plus s b, hsb.
So knowing the transitional, transition probabilities,
again this is a transitional probability.
Probability of going to the black state or having
a black pixel given that the previous pixel was wide.
And this is the probability of, the.
Current pixel being wide, given the previous one was wide.
[INAUDIBLE] staying in the, in the same state.
As we discussed, in encoding a scanned document.
It makes a lot of sense not to encode the 0s and 1s.
directly the black and white pixels
directly, but instead to encode the strands.
How many black pixels in a row and white pixels in a row we have.
So now we can build Huffman codes for the runs of 0s and runs of 1s.
These are one dimensionally the sense of you're looking at one scan line
at a time without considering the correlation with neighboring lines.
In order build the Huffman codes, we need to estimate the
probabilities of runs of white pixels and runs of black pixels.
So based on a large volume of documents, one can go and compute such probabilities.
And then, following the procedural steps that we
described earlier we can build two Huffman codes.
One for the runs of 0s and one for the runs of 1s.
Now this is not exactly what is happening within fax standard,
but instead the modified Huffman code is utilized.
The modification comes from the fact that we
want to constrain the number of possible lengths.
For an A4 document the largest trunk can be 1728 pixels.
Therefore, we need to build two Huffman codes each of
which would have a size of the alphabet equal 17, 28.
So these are huge codes, huge tables.
So to address this, issue.
The [INAUDIBLE] length is computed modular 64, so rl is the
the run length of zeros or ones, black or white pixels.
And it's here decomposed that a multiple of 64 plus t.
And here is the range of values for t, and the range values of m.
So, with this modulo 60 40 composition, we build one
Huffman table for m and these are called the make-up codes.
And one Huffman table for t, and these are called the terminating codes.
So we have two codes per color, therefore four such codes in total.
As I already mentioned for an A4 size document this
is the maximum length of, of pixel or of a run.
But if we want to deal with wider documents then
the standard provides optional codes to be able to do so.
So here are the Huffman Codes used by both G3 and G4 standards.
I'm not sure if you are able to read
the letters and numbers clearly, they are rather small.
These codes are present in any fax machine that is compliant with the standard.
Clearly the codes are both needed for both encoding and decoding a scanned document.
Recorded encoding of the run length is done
modular 64, so R L equals 64 times m plus t.
So here are the terminating codes.
They encode t and this is for the white
run length and here is for the black run length.
As one would expect, the probabilities of the various runs differ.
So runs of two and three black pixels here occur frequently, right?
The smaller the code word, the higher the probability of the symbol.
Similarly, the runs here of two to seven white pixels are frequently encountered.
They have small codewords.
Here you see the makeup codes.
So this codes in codes that multiples of 64.
Actually I joke sometimes when I teach this class here.
[UNKNOWN] And tell the students that they have to memorize these codes
in order to be able to find out the grade in the class.
We see here that we have runs of 0 white pixels and runs of 0 black pixels.
And the question is, why do we need to indicate 0 runs?
One reason for the 0 white run.
Is that the first run in the line is always a white run.
So if the first pixel in the line is a black pixel, then we have first
to encode a 0 white run and then the runs of the black pixels.
Also, after a make-up code is used, we expect, to receive a termination code.
So if we have run that is exactly in the integer multiple of 64, then
t is equal, t equals 0 and therefore we need a code word for that.
We also notice that there is an end of line.
Code here for both the white runs and the black runs.
And this is needed to synchronize the
operation of the decoder with that encoder.
So here we have a run of 64 there for m equals 1, the first line.
Then, in this particular table we go to m equals 40.
So 40 times 64 equals 2560, shown here.
And 27 core response to
1728 so m equals 27 here.
So let's say now we want to encode a run length [SOUND].
Of, let's say, 12 white pixels.
[SOUND] Then, since 12 is less than 64.
We're looking at the terminating code here.
So.
12 white pixels corresponds here.
So this is the codeword for this
particular symbol, the run of 12 white pixels.
If there's another example, we have a run
length of let's say, 140 black pixels.
This is greater than 64.
And 140 is equal to 2 times 64 plus 12.
So 2 times 64, this is 128.
So I go to the make-up code
and this is the black run length, so 128.
So the codeword is this long one.
I could have chosen a simpler one, I guess.
One, two, three, four, 1,1,0, two, three and then
here's kind of the residual so I need to terminating code.
So this is we go now to the other
table so this is the code for 12.
A run of 12 black pixels.
So to this we add that code which is 0000111.
So this is the code
that is used for 100, for a run of 140 black pixels.
In two dimensional codes, instead of reporting their run lengths.
Which, in terms of our Markov model, is the
length of the time we remain in one state.
We report, instead, the transition times, when
we move from one state to another state.
So if we look at this figure, we can encode it in two ways.
The one, the one we just described in previous slides.
So if we encode the first stroke,
then if you recall we assume that the first
pixel white so there's 0 run of white pixels.
Then a run of two of black pixels.
A run of three, a run of tnree.
And then on of eight.
Similarly, for the second row, [SOUND], utilizing the way of
the previous [UNKNOWN], is, again, a, 0 run of white pixels.
A run of one black pixels.
A run of eight, a run of three, a run of four.
Alternatively, we can encode the positions.
When a transition takes place, either from a white to
a black pixel or from a black to a white pixel.
The first pixel, it's an imaginary white pixel to the
left of the first pixel, so there's a white pixel here.
Therefore, the location of the transition actually is indicated by this dots.
So for the first row, there's a transition here.
The first peaks of occasion.
The second transition is here.
The third pixel location, the six, nine
pixel location and the nine pixel location.
Similar for the first row.
[SOUND] We go from white to black in the first pixel location.
Again, a transition at two, a transition at ten over here.
And a transition
at 15.
Now, since the rows are correlated, it would be easier
to encode these transition points in reference to the previous line.
Or its distance from the previous transition point.
Instead of in terms of its absolute location, this location, as we just did.
And this exactly the idea behind this scheme.
This so-called READ scheme, relative, element, address, designate.
And, actually within the standard is a modification
of these, so it's refered to as modified READ.
As, I already mentioned, READ exploits the correlation of image rows.
And then, it codes this transition points in reference to either the previous line.
Or the distance from the previous point in the same line.
