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Два простых способа настройки фрез для углового сращивания

Два простых способа настройки фрез для углового сращивания

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Обработка HDR. Обработка интерьерной съемки. Алексей Довгуля

Обработка HDR. Обработка интерьерной съемки. Алексей Довгуля

Channel: Liveclasses — мастер-классы 24/7

мастер-класс

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журавлев

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photoshop

00:00:04

good afternoon with you again, I’m Andrey

00:00:07

Zhuravlev and we continue to study the

00:00:09

Ada Photoshop program,

00:00:10

but more precisely, today we continue this

00:00:13

process that we started at the previous

00:00:15

master class,

00:00:16

last time we got acquainted with such a

00:00:20

wonderful color space as

00:00:23

club, but someone calls the space

00:00:24

someone- that’s what the color model says, and

00:00:27

today’s topic of our lesson is

00:00:29

adobe photoshop work in the lab, practice of

00:00:32

application,

00:00:33

but as always in any master class the

00:00:37

title of which is this

00:00:39

practice of application,

00:00:40

today we will discuss the subtleties of the

00:00:42

tricks, the nuances of course, providing

00:00:47

some small theoretical

00:00:50

basis for this with an explanation of the reasons why

00:00:52

certain processes occur, but

00:00:54

today we will try to analyze some

00:00:56

more cunning techniques than we examined

00:00:59

last time and I will begin my story with

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this topic:

00:01:04

myths and horror stories about paws,

00:01:08

I hope that samlab has already ceased to be a

00:01:11

horror story for you and having seen Well, for example,

00:01:14

some photograph like this,

00:01:18

if you discover that it is in the

00:01:24

lab color model, you won’t be

00:01:26

scared by the contents of the channels and abby

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difference color channels, which

00:01:31

contain encoded information about the

00:01:34

chromatic component,

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we discussed in detail in the last lesson

00:01:38

how these channels are arranged, what they mean

00:01:40

different brightnesses in these channels, how

00:01:44

can you work with this information using

00:01:46

curves, today we will naturally continue

00:01:48

this conversation, but I hope that these

00:01:51

strange pictures no longer inspire

00:01:54

such horror as when you first look at the

00:01:56

paws, there is little representation of an ordinary

00:01:59

image, to put it briefly,

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probably the majority horror stories related to

00:02:07

the weak relate to the loss of information

00:02:12

that can occur either when

00:02:14

working in the lab or, accordingly, when

00:02:16

transferring back and forth, well, let’s give the

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simplest example, the most obvious

00:02:21

where such horror stories come from, let’s

00:02:24

assume we have some kind of image,

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then we create an adjustment layer for

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curves, yes, this is a picture for now

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expression and start looking at the

00:02:36

red channel in the channels, we see that the histogram is

00:02:38

stretched over the entire range, that is, in the

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red channel there is brightness from 0

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to 255, the

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same picture is repeated in the

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green channel, the histogram is also stretched over the

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entire range, that is, also 256 levels,

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but also in the channel blue, similarly, if we

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take the number 256 and raise it to a cube,

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that is, we multiply the amount of brightness

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in the red channel by the amount of brightness in

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green, the amount of brightness in blue,

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we will get 16 million at the output there

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with some tail of combinations,

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these 16 million combinations by

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mistake are often called colors and you

00:03:22

can come across such an expression that

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the monitor transmits to us 16 something like millions of

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colors or in the eight-bit representation

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in the picture we have 16

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million colors,

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but let’s talk about why they are mistakenly

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called colors, let’s talk a little

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later, for now that having temporarily gotten rid

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of this adjustment layer of

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curves, we will transfer the image to the lab and

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now we will create curves in the lobby and

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look at the histogram in the bright channel, in

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principle, the histogram

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has not changed much, that is, they are stretched over the entire

00:04:03

range, although here, at first, beginners

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also have some fears fears

00:04:10

associated with the fact that the scale when working with

00:04:14

curves in the York sleep channel is

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graduated from 100 units, but

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we already talked about this last time at the last

00:04:21

master class, that in fact this is the

00:04:23

brightness channel of the paws if your picture is

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presented in eight-bit form 8 bits

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per channel, respectively, in the furious

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paw channel there will be 256. levels, that is,

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156 new levels are not going anywhere,

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it’s just this brightness scale from 0 to

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255, as we are used to measuring it in the toad

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in lobby 1, graduated by 100

00:04:49

conventional units, that’s all, that is, with a

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furious channel, everything is simple, this is such a

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horror story, very small, but then we

00:04:57

go with these difference channels and see that

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they contain images with very

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low contrast, but compared to the

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full contrast

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picture we are used to, and indeed, if

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we switch to the channel and we will see that there is

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a histogram here, but right from the

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very and and apparently the edges, taking into account here this

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small tail, well, at best, it is

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one quarter of the full range, that

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is, it turns out that

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three quarters of the once ranges of the channel

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hey bushes that small points do not

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correspond, that is, we have

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lost three quarters of the information in one of the channels

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and

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after that we go into the by channel here it’s

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a little wider, but also somewhere like that,

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about one third probably of the full

00:05:42

range and two thirds still fell from here

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three times less information in the channel by

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4 times less information in the channel hey,

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well, if you compare this with a toad

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and now people start to feel sad, it seems

00:05:59

how our information

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has decreased 12 times, but in general, of course, when it is

00:06:07

transferred back into the paws, the information is

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partially lost; another question is

00:06:13

how much of it is lost,

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let me show how the

00:06:18

loss occurs; what was more clearly

00:06:20

visible? I’ll open this image,

00:06:23

this is a synthetic picture that is, it was

00:06:27

artificially made and this picture

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contains all those same 16

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million there with a tail of the combination

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red green blue which can be specified

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in eight-bit mode if you look closely at the

00:06:41

channels you will notice how it is

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arranged in the red channel we have one two

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three 4 5 6 7 8

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well here 8 that is 16 gradients of these

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vertical in the green channel we have

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16 horizontal gradients respectively

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that is it turns out to be a

00:07:02

square and where the gradient is superimposed on the gradient

00:07:06

and here the wind is repeated in general the

00:07:09

same combination options and besides this

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there is also this a set of dies is already

00:07:20

in the blue channel, also respectively 16

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pieces, so at the intersection of

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all these channels we get all

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possible combinations, what will happen

00:07:33

if we transfer this picture to the lab,

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let me use the command edit

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envelopes profile convert to

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profile and turn off the checkbox here and us

00:07:44

die erde why this needs to be done, I

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’ll explain a little later and you and I will select

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weak here and say okay, here we have

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a transformation, you can see how

00:07:59

the channels are arranged that are here, well, it’s clear

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that Vlad, in general, everything

00:08:03

turned out quite cleanly, and now

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let’s do the reverse conversion,

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we’ll give this is in the rye by let

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’s say this envelope to profile,

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out of habit I’ll transfer this to the

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srgb profile itself, which is so popular

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and simple and once again we’ll say ok

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if we now look at the contents of

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these rectangles, we’ll see that

00:08:40

pasteurization is slowly starting here, that is, we can simply

00:08:42

increase here, with barely

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a hint, such

00:08:47

heterogeneity of vertical stripes is pecked, and

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if this picture is analyzed by

00:08:54

Photoshop, in my opinion, it doesn’t know how to

00:08:56

do such an analysis, but such an analysis was done

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using a special program, it

00:09:02

turns out that after such a

00:09:05

transformation it will be

00:09:06

translated from behind the fish in the forehead and translated

00:09:08

back from the paws in the rye by

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out of 16 million combinations,

00:09:13

approximately half are lost, that is,

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giving a real translation into the paws

00:09:19

back leads to the fact that about

00:09:21

half of the combination is lost, people then

00:09:25

grab their heads and

00:09:27

say how are you,

00:09:28

how can you live after this, the gap can work like this

00:09:31

and so here is the time to remember

00:09:34

that it is wrong to call 16 million

00:09:38

colors, according to various estimates, a person is

00:09:41

able to distinguish,

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well, there are 10-20 thousand colors, this is the

00:09:47

estimate along the lower edge

00:09:49

to a maximum of about 200 thousand colors,

00:09:54

the estimates are very different because in general

00:09:57

our perception of gradation as in brightness

00:10:00

so in terms of saturation in hue, it is not

00:10:03

linear, that is, well, roughly speaking, at

00:10:06

different brightnesses we perceive a different

00:10:08

number of gradations of saturation, and

00:10:10

depending on the hue, the

00:10:12

perception and amount of perceived

00:10:15

gradation of brightness changes, that is, there is a very

00:10:17

tricky complex relationship and of

00:10:19

course no one put a person and I didn’t

00:10:21

try to show him 200 thousand different

00:10:23

patches,

00:10:25

the estimate is in general about several

00:10:27

tens of thousands of colors, you and I are able to

00:10:31

distinguish,

00:10:32

therefore, even if there are 8 million

00:10:35

combinations, red green blue is

00:10:37

redundant for our vision, extremely

00:10:40

redundant and in real photographs,

00:10:43

especially if we take into account the presence of noise in the photograph,

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disappearance Some of the possible

00:10:50

combinations of the toad, replacing them with similar ones will

00:10:54

not lead to a visual change in the picture,

00:10:57

that is, if we again return

00:11:00

from an artificially made patch,

00:11:02

some real photographs like this, for

00:11:05

example, and we will conduct an experiment with you, let

00:11:09

’s assume there ten times transferring

00:11:13

this picture there back, that is, we will

00:11:15

and have healed, transfer to the lab and then

00:11:17

transfer back to Urzhum

00:11:18

purely technically, then we will compare the two

00:11:21

images by nesting, for example, on top of each

00:11:23

other in the difference mode and enhancing the

00:11:26

difference, of course we will notice that these

00:11:28

pictures are different, but visually this

00:11:33

difference will not appear in any way if

00:11:38

you try conduct such an experiment

00:11:40

and you will see that the difference

00:11:46

has accumulated quite large,

00:11:48

most likely this happened precisely because you

00:11:51

had this

00:11:55

checkbox enabled in the Russian version, it’s called

00:11:56

dithering;

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this noise, let's

00:12:07

look a little further, but the 2nd horror story is

00:12:12

also related to the disappearance of information, it

00:12:16

concerns the contrasts that were in the

00:12:20

channels, we have already looked at

00:12:23

this photo with you in one of the previous

00:12:25

master classes devoted to

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working with image channels and we saw

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that in the blue channel it is very well

00:12:32

preserved the texture of the tablecloth is very clearly visible,

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while the red

00:12:36

and green channels are practically empty; in the

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red there is just a blatant hole in the

00:12:41

green channel; here, in

00:12:44

general, it’s neither fish nor fowl, and yet in the

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blue channel the tablecloths

00:12:48

survive

00:12:49

if we now convert this image

00:12:52

into lab let's say okay,

00:12:55

then we will see that the appearance of the tablecloth has been

00:12:59

approximately preserved, but such an obvious

00:13:01

clear texture as

00:13:02

could be found in the blue channel is

00:13:05

no longer present and the bright dream channel of the paws generally

00:13:10

contains, well, almost incomprehensibly, it is

00:13:13

not clear that this is very reminiscent of the

00:13:15

contents of the green channel before translation

00:13:17

then there is very low contrast, there seems to be

00:13:19

almost no detail in the by channel, there are

00:13:23

some seemingly details in also

00:13:25

very low contrast, and those who have

00:13:29

already communicated with the fish channels are used to

00:13:32

somehow extracting information from them that is

00:13:34

useful for work; in this place,

00:13:36

there is also some fear and what to do

00:13:40

next? Well, we had details in the blue

00:13:42

channel, but they disappeared, in fact, they did not

00:13:45

disappear. You need to understand that any

00:13:47

representation of both color, that is, the color of an

00:13:51

individual pixel, and the

00:13:53

array of distribution of these colors, that

00:13:55

is, the picture as a whole, be it Jiri Bella

00:13:58

dusts a moment this and you can also

00:14:01

add here these are just different options for

00:14:04

presenting information on the 1st floor, simply

00:14:08

depending on and in which version of

00:14:10

this information you present some

00:14:15

details that can appear more

00:14:17

openly or less openly, that is,

00:14:20

Vlad’s presentation, this texture does

00:14:22

not appear so clearly how did you live, but if

00:14:24

we translate this picture back,

00:14:26

let's say profile again to the envelope and

00:14:29

recalculate it to ourselves,

00:14:31

we will see that

00:14:34

all the detail that we observed

00:14:36

earlier has returned to the blue channel, that is, don’t be alarmed, until in the lobby, well, it’s

00:14:40

not so bright, not so interesting,

00:14:44

some are presented

00:14:46

brightness contrast and which can be found

00:14:47

in the mode, but nevertheless they do not

00:14:50

disappear anywhere, that is, we love to turn everything inside out and

00:14:52

you will see that all the detail

00:14:55

is restored.

00:14:56

Well, let’s finish by still

00:15:01

showing some real fear, otherwise we are

00:15:04

all about far-fetched phobias There is such a horror story that when

00:15:10

transferred to the lab, pasteurization begins

00:15:14

on artificial gradients, artificially

00:15:18

made gradients, or on very very

00:15:20

smooth, very smooth gradients that

00:15:23

sometimes appear in photographs very

00:15:26

often, by the way, such gradients appear

00:15:29

against the background if the background is very homogeneous and

00:15:34

there is naturally some kind of

00:15:35

gradient on it illumination, here we get

00:15:37

such a major transition, here the transition is

00:15:41

made artificially, let’s increase,

00:15:46

for example, this fragment and if we

00:15:49

look at the channels

00:15:51

red channel green channel blue channel,

00:15:54

well, it’s clear that there is some slight

00:15:58

hint of gradation because after

00:16:00

all the gradient is a long difference in

00:16:02

brightness, insignificant work we

00:16:05

eight-bit mode and for this reason

00:16:09

we have all 256 levels per channel, one

00:16:13

difference here and even less, so

00:16:16

yes here we see such a barely

00:16:19

noticeable pasteurization due to the fact that

00:16:21

quite wide areas of the gradient are

00:16:23

painted in a uniform color

00:16:25

if we now transform this

00:16:27

image in the lab

00:16:30

we convert to a profile, again without

00:16:32

this checkbox, we’ll say ok,

00:16:36

but if you look in the lobby, in general there is

00:16:39

no special banding of the channels here

00:16:42

and both are generally more uniform, it feels like

00:16:45

they are almost evenly colored in the

00:16:48

bright dream channel, a smooth soft gradient,

00:16:50

but the opposite the transition will give

00:16:57

such an effect, the striping has clearly begun

00:17:00

in red, we have very

00:17:03

decent stripes in the mushroom, we have

00:17:06

outright

00:17:08

pasteurization in the blue channel, yes,

00:17:11

indeed, this is not a horror story, this is a

00:17:14

real effect, transfer to the lab and back,

00:17:18

these are two conversion procedures from the

00:17:22

profiles of the street profile, both there is no profile, there is

00:17:26

only one representation in Photoshop,

00:17:27

but this transformation effects the

00:17:31

mathematical transformation is quite

00:17:32

complex and

00:17:33

on such artificial gradients the

00:17:36

rounding errors come out very

00:17:39

clearly, that is, there really is such a

00:17:43

nuisance,

00:17:44

however, this nuisance can be

00:17:46

dealt with quite effectively, let

00:17:48

me close the picture and open it again so that

00:17:50

we can again, starting from scratch,

00:17:57

defeating this kind of low-contrast

00:18:00

pasteurization on a gradient can be achieved in one

00:18:03

single way by adding noise to it; this is exactly what the

00:18:06

checkbox that

00:18:10

I stubbornly unchecked before does this, this

00:18:12

checkbox and uni, that is, dithering, it

00:18:16

adds noise, put this key, transfer it

00:18:20

to lab and let's once again

00:18:23

transfer the profile envelopes back into the toad,

00:18:25

let it stand again, let's create

00:18:28

ok, go to the red channel and see that

00:18:32

everything is not so loud and noisy and return not so

00:18:35

loud the posts are drawn, the effect of

00:18:38

softening pasteurization appeared due to the

00:18:41

fact that on this gradient has added

00:18:42

noise, I’ll increase it on purpose now,

00:18:44

but I don’t know how it will be a video

00:18:47

because the codec will then process all this,

00:18:49

but in theory, with such an

00:18:51

increase it should be noticeable that

00:18:53

our gradient is not perfectly uniform,

00:18:56

that such small concessions have appeared on it

00:19:00

the effect of adding noise, it

00:19:06

knocks down very effectively, it knocks down

00:19:08

somewhat

00:19:09

red banding, and if you see that

00:19:16

in the picture you have some

00:19:18

smooth transitions, then when

00:19:20

converting from profile to profile or when

00:19:23

converting there, jumping from some

00:19:26

profile in the lab, you definitely need a tattoo here

00:19:29

put a tick plus 9 in order to

00:19:33

compensate for pasteurization what is the

00:19:40

difference between well, let’s first talk

00:19:44

about the difference between the picture

00:19:49

the original picture that you converted many times

00:19:51

into paws back I said

00:19:54

that you can experiment with the

00:19:56

example with this image

00:19:59

big difference big difference between

00:20:02

source and multiple times converted

00:20:05

image can accumulate if

00:20:07

each time during conversion this key was

00:20:10

active, that is, it turns out that every

00:20:13

time during conversion noise is added

00:20:16

if you do 10 conversions there and

00:20:19

10 conversions back twenty

00:20:21

times, additional

00:20:23

noise is added to your picture, well, of course, this is early

00:20:26

or it looks late, that is, it will become

00:20:28

noticeable that the picture is so clumsy and

00:20:30

it turns out so if for some

00:20:34

reason you need to

00:20:36

transfer the image several times to the lab and back in the

00:20:39

fish, I can’t because I can’t

00:20:42

consciously think of what the reason could be,

00:20:44

well, let’s assume it’s hot

00:20:47

as they say in this case, I highly

00:20:51

recommend that all conversions

00:20:53

except the last one be done with the

00:20:55

add noise checkbox unchecked,

00:20:58

and only the last time when you

00:21:00

finally get out of your paws and translate

00:21:02

the picture you have broken, but you will have to

00:21:06

do it simply because monitors

00:21:09

that work head-on do not invented, it

00:21:10

cannot be invented, that is, of course, the

00:21:13

user still needs to make an

00:21:15

image of the fish, or maybe there,

00:21:18

okay in a moment, if you are dealing with this

00:21:20

division for printing,

00:21:21

at the last transformation, this

00:21:24

key must be set, then the noise will be

00:21:26

added once, this will be enough

00:21:29

to soften

00:21:32

arising on smooth gradients,

00:21:35

pasteurization, but at the same time there

00:21:38

will not be much of your opinion on the picture, and at the end of

00:21:41

this conversation, a topic that I have already

00:21:43

touched on

00:21:44

regarding the difference between the commands image

00:21:48

mod for paws and ball mod for fish,

00:21:52

that is, translation expressed directly through

00:21:56

commands from the image mod menu and translation

00:21:59

through the command there is convert to profile

00:22:02

editing the conversion to the profile,

00:22:05

there is also such a thing, it’s not a horror story, it’s cute and there is

00:22:10

a misconception that there

00:22:12

is a fundamental

00:22:13

difference between these commands, and it’s correct

00:22:17

to do nothing like that only with the help of converting a profile,

00:22:20

and I talked about this in the master class

00:22:22

regarding the color management system, let me

00:22:25

just remind you

00:22:26

of the settings we have in the

00:22:29

profile envelope command, which profile

00:22:31

to convert, which engine will be how to

00:22:34

get rid of out-of-coverage colors,

00:22:36

whether to compensate for the black point and whether

00:22:39

or not to add noise, let’s talk about the

00:22:42

end and go to the color

00:22:45

settings settings colors that we

00:22:48

see here we see that in this section of

00:22:51

conversion absence we have the same

00:22:54

set of parameters what engine

00:22:56

what to do with them with cotton colors

00:22:58

whether to use black dot compensation

00:23:00

and whether or not to add noise to an 8-

00:23:05

bit image

00:23:06

yes this last checkbox is it is connected

00:23:09

with the collaboration between Photoshop and the

00:23:13

Adobe After Effects program, so

00:23:16

for us it does not play a role, this is the

00:23:20

same set of settings that is in the

00:23:22

pro envelope command, convert convert

00:23:24

to profile, if these settings match,

00:23:29

what is in the color settings

00:23:31

is what is entered in the profile envelope there will

00:23:34

be no difference when translating a picture,

00:23:37

well, naturally, from the profile that is

00:23:39

selected, there will be no difference when translating a

00:23:42

picture using the command image model and

00:23:45

using the command goes to the profile envelope,

00:23:48

why now when I was

00:23:52

experimenting when I translated something I

00:23:54

used the profile envelope command

00:23:56

because I wanted to

00:23:57

convert removing the noise reduction, but

00:24:01

at the same time I didn’t want to change the

00:24:02

color settings,

00:24:04

that is, the commands for the profile envelope were made

00:24:07

so that it was possible to

00:24:09

convert the image from the

00:24:12

profile profiles with some individual

00:24:15

settings

00:24:17

without changing the general color settings.

00:24:19

Well, for starters, we’ll assume that with

00:24:24

We've figured out the main lab horror stories

00:24:26

and let's move on to the next topic

Description:

Смотреть весь мастер-класс «Adobe Photoshop: цветовая модель Lab. Практика применения» – https://liveclasses.ru/course/graphics/lab_color_model_practice/ Научись работать в пространстве Lab Все мастер-классы преподавателя: https://liveclasses.ru/teachers/andrey_zhuravlev/ Онлайн-курсы преподавателя: https://www.profileschool.ru/teacher/andrey-zhuravlev Telegram канал LiveСlasses: графика и дизайн: https://liveclasses.ru/tg/graphics/

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