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Why do we always divide our RGB values by 255? I know that the range is from [0-1]. But why dive only by 255? Can anyone please explain me the concepts of RGB values?
RGB (Red, Green, Blue) are 8 bit each.
The range for each individual colour is 0-255 (as 2^8 = 256 possibilities).
The combination range is 256*256*256.
By dividing by 255, the 0-255 range can be described with a 0.0-1.0 range where 0.0 means 0 (0x00) and 1.0 means 255 (0xFF).
This is a bit of a generic question since it can be specific to the platform and even to the method. It really comes down to math and getting a value between 0-1. Since 255 is the maximum value, dividing by 255 expresses a 0-1 representation.
Each channel (Red, Green, and Blue are each channels) is 8 bits, so they are each limited to 256, in this case 255 since 0 is included. As the reference shows, systems typically use values between 0-1 when using floating point values.
http://en.wikipedia.org/wiki/RGB_color_model
See Numeric Representations.
These ranges may be quantified in several different ways: From 0 to 1,
with any fractional value in between. This representation is used in
theoretical analyses, and in systems that use floating point
representations. Each color component value can also be written as a
percentage, from 0% to 100%. In computers, the component values are
often stored as integer numbers in the range 0 to 255, the range that
a single 8-bit byte can offer. These are often represented as either
decimal or hexadecimal numbers. High-end digital image equipment are
often able to deal with larger integer ranges for each primary color,
such as 0..1023 (10 bits), 0..65535 (16 bits) or even larger, by
extending the 24-bits (three 8-bit values) to 32-bit, 48-bit, or
64-bit units (more or less independent from the particular computer's
word size).
RGB values are usually stored as integers to save memory. But doing math on colors is usually done in float because it's easier, more powerful, and more precise. The act of converting floats to integers is called "Quantization", and it throws away precision.
Typically, RGB values are encoded as 8-bit integers, which range from 0 to 255. It's an industry standard to think of 0.0f as black and 1.0f as white (max brightness). To convert [0, 255] to [0.0f, 1.0f] all you have to do is divide by 255.0f.
If you care, this is the formula to convert back to integer: (int)floor(x * 255.0f + 0.5f). But first clamp x to [0.0f, 1.0f] if necessary.
The RGB value goes up from 0 to 255 because it takes up exactly one byte of data. One byte is equal to 8 bits, and each bit represents either a 0 or a 1. This makes 0 in 8 bit binary: 00000000 and 255 11111111. The last bit says if there is a 1 in the value. The second last says if there is a 2 in the value. The third last says if there is a 4 in the value, and so on doubling every time. If you add up all of the small values that are present, you get the total value. For example,
=10110101
=1*128 + 0*64 + 1*32 + 1*16 + 0*8 + 1*4 + 0*2 + 1*1
=128 + 32 + 16 + 4 + 1
=181
This means that 10110101 in binary equals 181 in decimal form.
Given that each Ocket is nowadays made of 8 bits ( binary digit)
Suppose we have an Ocket filled like this :
1 0 1 0 0 1 0 1
for each bit you get 2 possibilities : 0 or 1
2 x 2 x 2 x 2 x 2 x 2 x 2 x 2 = 2^8 = 256
total : 256
And for hexadecimal colors :
given that you have 3 couples of characters, dash excluded => ex: #00ff00
0, 1, 2, 3, 4 , 5, 6, 7, 8, 9, a, b, c, d, e, f = 16 possibilities
16 x 16 = 256
R V B = color
256 x 256 x 256 = 16 777 216 colors)
It makes the vector operations simpler... Imagine you have image and you want to change its color to red. With vectors you can just take every pixel and multiply it by (1.0, 0.0, 0.0)
P * (1.0, 0.0, 0.0)
Otherwise it just adds unnecessary steps (in this case dividing it by 255)
P * (255, 0, 0) / 255
And imagine using more complex filters, the unnecessary steps would stack up...
Related
As is evident by the MSDN description of decimal certain precision ranges have the same amount of storage bytes assigned to them.
What I don't understand is that there are differences in the sizes of the range. How the range from 1 to 9 of 5 storage bytes has a width of 9, while the range from 10 to 19 of 9 storage bytes has a width of 10. Then the next range of 13 storage bytes has a width of 9 again, while the next has a width of 10 again.
Since the storage bytes increase by 4 every time, I would have expected all of the ranges to be the same width. Or maybe the first one to be smaller to reserve space for the sign or something but from then on equal in width. But it goes from 9 to 10 to 9 to 10 again.
What's going on here? And if it would exist, would 21 storage bytes have a precision range of 39-47 i.e. is the pattern 9-10-9-10-9-10...?
would 21 storage bytes have a precision range of 39-47
No. 2 ^ 160 = 1,461,501,637,330,902,918,203,684,832,716,283,019,655,932,542,976 - which has 49 decimal digits. So this hypothetical scenario would cater for a precision range of 39-48 (as a 20 byte integer would not be big enough to hold any 49 digit numbers larger than that)
The first byte is reserved for the sign.
01 is used for positive numbers; 00 for negative.
The remainder stores the value as an integer. i.e. 1.234 would be stored as the integer 1234 (or some multiple of 10 of this dependant on the declared scale)
The length of the integer is either 4, 8, 12 or 16bytes depending on the declared precision. Some 10 digit integers can be stored in 4 bytes however to get the whole range in would overflow this so it needs to go to the next step up.
And so on.
2^32 = 4,294,967,295 (10 digits)
2^64 = 18,446,744,073,709,551,616 (20 digits)
2^96 = 79,228,162,514,264,337,593,543,950,336 (29 digits)
2^128 = 340,282,366,920,938,463,463,374,607,431,768,211,456 (39 digits)
You need to use DBCC PAGE to see this, casting the column as binary does not give you the storage representation. Or use a utility like SQL Server internals viewer.
CREATE TABLE T(
A DECIMAL( 9,0),
B DECIMAL(19,0),
C DECIMAL(28,0) ,
D DECIMAL(38,0)
);
INSERT INTO T VALUES
(999999999, 9999999999999999999, 9999999999999999999999999999, 99999999999999999999999999999999999999),
(-999999999, -9999999999999999999, -9999999999999999999999999999, -99999999999999999999999999999999999999);
Shows the first row stored as
And the second as
Note that the values after the sign bit are byte reversed. 0x3B9AC9FF = 999999999
How can I compress a row of integers into something shorter ?
Like:
Input: '1 2 4 5 3 5 2 3 1 2 3 4' -> Algorithm -> Output: 'X Y Z'
and can get it back the other way around? ('X Y Z' -> '1 2 4 5 3 5 2 3 1 2 3 4')
Note:Input will only contain numbers between 1-5 and the total string of number will be 10-16
Is there any way I can compress it to 3-5 numbers?
Here is one way. First, subtract one from each of your little numbers. For your example input that results in
0 1 3 4 2 4 1 2 0 1 2 3
Now treat that as the base-5 representation of an integer. (You can choose either most significant digit first or last.) Calculate the number in binary that means the same thing. Now you have a single integer that "compressed" your string of little numbers. Since you have shown no code of your own, I'll just stop here. You should be able to implement this easily.
Since you will have at most 16 little numbers, the maximum resulting value from that algorithm will be 5^16 which is 152,587,890,625. This fits into 38 bits. If you need to store smaller numbers than that, convert your resulting value into another, larger number base, such as 2^16 or 2^32. The former would result in 3 numbers, the latter in 2.
#SergGr points out in a comment that this method does not show the number of integers encoded. If that is not stored separately, that can be a problem, since the method does not distinguish between leading zeros and coded zeros. There are several ways to handle that, if you need the number of integers included in the compression. You could require the most significant digit to be 1 (first or last depends on where the most significant number is.) This increases the number of bits by one, so you now may need 39 bits.
Here is a toy example of variable length encoding. Assume we want to encode two strings: 1 2 3 and 1 2 3 0 0. How the results will be different? Let's consider two base-5 numbers 321 and 00321. They represent the same value but still let's convert them into base-2 preserving the padding.
1 + 2*5 + 3*5^2 = 86 dec = 1010110 bin
1 + 2*5 + 3*5^2 + 0*5^3 + 0*5^4 = 000001010110 bin
Those additional 0 in the second line mean that the biggest 5-digit base-5 number 44444 has a base-2 representation of 110000110100 so the binary representation of the number is padded to the same size.
Note that there is no need to pad the first line because the biggest 3-digit base-5 number 444 has a base-2 representation of 1111100 i.e. of the same length. For an initial string 3 2 1 some padding will be required in this case as well, so padding might be required even if the top digits are not 0.
Now lets add the most significant 1 to the binary representations and that will be our encoded values
1 2 3 => 11010110 binary = 214 dec
1 2 3 0 0 => 1000001010110 binary = 4182 dec
There are many ways to decode those values back. One of the simplest (but not the most efficient) is to first calculate the number of base-5 digits by calculating floor(log5(encoded)) and then remove the top bit and fill the digits one by one using mod 5 and divide by 5 operations.
Obviously such encoding of variable-length always adds exactly 1 bit of overhead.
Its call : polidatacompressor.js but license will be cost you, you have to ask author about prices LOL
https://github.com/polidatacompressor/polidatacompressor
Ncomp(65535) will output: 255, 255 and when you store this in database as bytes you got 2 char
another way is to use "Hexadecimal aka base16" in javascript (1231).toString(16) give you '4cf' in 60% situation it compress char by -1
Or use base10 to base64 https://github.com/base62/base62.js/
4131 --> 14D
413131 --> 1Jtp
I see this in Wikipedia log 224 = 7.22.
I have no idea why we should calculate 2^24 and why we should take log10......I really really need your help.
why floating-points number's significant numbers is 7 or 6 (?)
Consider some thoughts employing the Pigeonhole principle:
binary32 float can encode about 232 different numbers exactly. The numbers one can write in text like 42.0, 1.0, 3.1415623... are infinite, even if we restrict ourselves to a range like -1038 ... +1038. Any time code has a textual value like 0.1f, it is encoded to a nearby float, which may not be the exact same text value. The question is: how many digits can we code and still maintain distinctive float?
For the various powers-of-2 range, 223 (8,388,608) values are normally linearly encoded.
Example: In the range [1.0 ... 2.0), 223 (8,388,608) values are linearly encoded.
In the range [233 or 8,589,934,592 ... 234 or 17,179,869,184), again, 223 (8,388,608) values are linearly encoded: 1024.0 apart from each other. In the sub range [9,000,000,000 and 10,000,000,000), there are about 976,562 different values.
Put this together ...
As text, the range [1.000_000 ... 2.000_000), using 1 lead digit and 6 trailing ones, there are 1,000,000 different values. Per #3, In the same range, with 8,388,608 different float exist, allowing each textual value to map to a different float. In this range we can use 7 digits.
As text, the range [9,000,000 × 103 and 10,000,000 × 103), using 1 lead digit and 6 trailing ones, there are 1,000,000 different values. Per #4, In the same range, there are less than 1,000,000 different float values. Thus some decimal textual values will convert to the same float. In this range we can use 6, not 7, digits for distinctive conversions.
The worse case for typical float is 6 significant digits. To find the limit for your float:
#include <float.h>
printf("FLT_DIG = %d\n", FLT_DIG); // this commonly prints 6
... no idea why we should calculate 2^24 and why we should take log10
224 is a generalization as with common float and its 24 bits of binary precision, that corresponds to fanciful decimal system with 7.22... digits. We take log10 to compare the binary float to decimal text.
224 == 107.22...
Yet we should not take 224. Let us look into how FLT_DIG is defined from C11dr §5.2.4.2.2 11:
number of decimal digits, q, such that any floating-point number with q decimal digits can be rounded into a floating-point number with p radix b digits and back again without change to the q decimal digits,
p log10 b ............. if b is a power of 10
⎣(p − 1) log10 _b_⎦.. otherwise
Notice "log10 224" is same as "24 log10 2".
As a float, the values are distributed linearly between powers of 2 as shown in #2,3,4.
As text, values are distributed linearly between powers of 10 like a 7 significant digit values of [1.000000 ... 9.999999]*10some_exponent.
The transition of these 2 groups happen at different values. 1,2,4,8,16,32... versus 1,10,100, ... In determining the worst case, we subtract 1 from the 24 bits to account for the mis-alignment.
⎣(p − 1) log10 _b_⎦ --> floor((24 − 1) log10(2)) --> floor(6.923...) --> 6.
Had our float used base 10, 100, or 1000, rather than very common 2, the transition of these 2 groups happen at same values and we would not subtract one.
An IEEE 754 single-precision float has a 24-bit mantissa. This means it has 24 binary bits' worth of precision.
But we might be interested in knowing how many decimal digits worth of precision it has.
One way of computing this is to consider how many 24-bit binary numbers there are. The answer, of course, is 224. So these binary numbers go from 0 to 16777215.
How many decimal digits is that? Well, log10 gives you the number of decimal digits. log10(224) is 7.2, or a little more than 7 decimal digits.
And look at that: 16777215 has 8 digits, but the leading digit is just 1, so in fact it's only a little more than 7 digits.
(Of course this doesn't mean we can represent only numbers from 0 to 16777215! It means we can represent numbers from 0 to 16777215 exactly. But we've also got the exponent to play with. We can represent numbers from 0 to 1677721.5 more or less exactly to one place past the decimal, numbers from 0 to 167772.15 more or less exactly to two decimal points, etc. And we can represent numbers from 0 to 167772150, or 0 to 1677721500, but progressively less exactly -- always with ~7 digits' worth of precision, meaning that we start losing precision in the low-order digits to the left of the decimal point.)
The other way of doing this is to note that log10(2) is about 0.3. This means that 1 bit corresponds to about 0.3 decimal digits. So 24 bits corresponds to 24 × 0.3 = 7.2.
(Actually, IEEE 754 single-precision floating point explicitly stores only 23 bits, not 24. But there's an implicit leading 1 bit in there, so we do get the effect of 24 bits.)
Let's start a little smaller. With 10 bits (or 10 base-2 digits), you can represent the numbers 0 upto 1023. So you can represent up to 4 digits for some values, but 3 digits for most others (the ones below 1000).
To find out how many base-10 (decimal) digits can be represented by a bunch of base-2 digits (bits), you can use the log10() of the maximum representable value, i.e. log10(2^10) = log10(2) * 10 = 3.01....
The above means you can represent all 3 digit — or smaller — values and a few 4 digits ones. Well, that is easily verified: 0-999 have at most 3 digits, and 1000-1023 have 4.
Now take 24 bits. In 24 bits you can store log10(2^24) = 24 * log(2) base-10 digits. But because the top bit is always the same, you can in fact only store log10(2^23) = log10(8388608) = 6.92. This means you can represent most 7 digits numbers, but not all. Some of the numbers you can represent faithfully can only have 6 digits.
The truth is a bit more complicated though, because exponents play role too, and some of the many possible larger values can be represented too, so 6.92 may not be the exact value. But it gets close, and can nicely serve as a rule of thumb, and that is why they say that single precision can represent 6 to 7 digits.
How many bits out of 64 is assigned to integer part and fractional part in double. Or is there any rule to specify it?
Note: I know I already replied with a comment. This is for my own benefit as much as the OPs; I always learn something new when I try to explain it.
Floating-point values (regardless of precision) are represented as follows:
sign * significand * βexp
where sign is 1 or -1, β is the base, exp is an integer exponent, and significand is a fraction. In this case, β is 2. For example, the real value 3.0 can be represented as 1.102 * 21, or 0.112 * 22, or even 0.0112 * 23.
Remember that a binary number is a sum of powers of 2, with powers decreasing from the left. For example, 1012 is equivalent to 1 * 22 + 0 * 21 + 1 * 20, which gives us the value 5. You can extend that past the radix point by using negative powers of 2, so 101.112 is equivalent to
1 * 22 + 0 * 21 + 1 * 20 + 1 * 2-1 + 1 * 2-2
which gives us the decimal value 5.75. A floating-point number is normalized such that there's a single non-zero digit prior to the radix point, so instead of writing 5.75 as 101.112, we'd write it as 1.01112 * 22
How is this encoded in a 32-bit or 64-bit binary format? The exact format depends on the platform; most modern platforms use the IEEE-754 specification (which also specifies the algorithms for floating-point arithmetic, as well as special values as infinity and Not A Number (NaN)), however some older platforms may use their own proprietary format (such as VAX G and H extended-precision floats). I think x86 also has a proprietary 80-bit format for intermediate calculations.
The general layout looks something like the following:
seeeeeeee...ffffffff....
where s represents the sign bit, e represents bits devoted to the exponent, and f represents bits devoted to the significand or fraction. The IEEE-754 32-bit single-precision layout is
seeeeeeeefffffffffffffffffffffff
This gives us an 8-bit exponent (which can represent the values -126 through 127) and a 22-bit significand (giving us roughly 6 to 7 significant decimal digits). A 0 in the sign bit represents a positive value, 1 represents negative. The exponent is encoded such that 000000012 represents -126, 011111112 represents 0, and 111111102 represents 127 (000000002 is reserved for representing 0 and "denormalized" numbers, while 111111112 is reserved for representing infinity and NaN). This format also assumes a hidden leading fraction bit that's always set to 1. Thus, our value 5.75, which we represent as 1.01112 * 22, would be encoded in a 32-bit single-precision float as
01000000101110000000000000000000
|| || |
|| |+----------+----------+
|| | |
|+--+---+ +------------ significand (1.0111, hidden leading bit)
| |
| +---------------------------- exponent (2)
+-------------------------------- sign (0, positive)
The IEEE-754 double-precision float uses 11 bits for the exponent (-1022 through 1023) and 52 bits for the significand. I'm not going to bother writing that out (this post is turning into a novel as it is).
Floating-point numbers have a greater range than integers because of the exponent; the exponent 127 only takes 8 bits to encode, but 2127 represents a 38-digit decimal number. The more bits in the exponent, the greater the range of values that can be represented. The precision (the number of significant digits) is determined by the number of bits in the significand. The more bits in the significand, the more significant digits you can represent.
Most real values cannot be represented exactly as a floating-point number; you cannot squeeze an infinite number of values into a finite number of bits. Thus, there are gaps between representable floating point values, and most values will be approximations. To illustrate the problem, let's look at an 8-bit "quarter-precision" format:
seeeefff
This gives us an exponent between -7 and 8 (we're not going to worry about special values like infinity and NaN) and a 3-bit significand with a hidden leading bit. The larger our exponent gets, the wider the gap between representable values gets. Here's a table showing the issue. The left column is the significand; each additional column shows the values we can represent for the given exponent:
sig -1 0 1 2 3 4 5
--- ---- ----- ----- ----- ----- ----- ----
000 0.5 1 2 4 8 16 32
001 0.5625 1.125 2.25 4.5 9 18 36
010 0.625 1.25 2.5 5 10 20 40
011 0.6875 1.375 2.75 5.5 11 22 44
100 0.75 1.5 3 6 12 24 48
101 0.8125 1.625 3.25 6.5 13 26 52
110 0.875 1.75 3.5 7 14 28 56
111 0.9375 1.875 3.75 7.5 15 30 60
Note that as we move towards larger values, the gap between representable values gets larger. We can represent 8 values between 0.5 and 1.0, with a gap of 0.0625 between each. We can represent 8 values between 1.0 and 2.0, with a gap of 0.125 between each. We can represent 8 values between 2.0 and 4.0, with a gap of 0.25 in between each. And so on. Note that we can represent all the positive integers up to 16, but we cannot represent the value 17 in this format; we simply don't have enough bits in the significand to do so. If we add the values 8 and 9 in this format, we'll get 16 as a result, which is a rounding error. If that result is used in any other computation, that rounding error will be compounded.
Note that some values cannot be represented exactly no matter how many bits you have in the significand. Just like 1/3 gives us the non-terminating decimal fraction 0.333333..., 1/10 gives us the non-terminating binary fraction 1.10011001100.... We would need an infinite number of bits in the significand to represent that value.
a double on a 64 bit machine, has one sign bit, 11 exponent bits and 52 fractional bits.
think (1 sign bit) * (52 fractional bits) ^ (11 exponent bits)
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First of all i woild like to point out that i am not native speaker and i really need some terms used more commonly.
And the second thing i would like to mention is that i am not a math genious. I am really trying to understand everything about programming.. but ieee-754 makes me think that it'll never happan.. its full of mathematical terms i don't understand..
What is precision? What is it used for? What is mantissa and what is mantissa used for? How to determine the range of float/double by their size? What is ± symbol (Plus-minus) used for? (i believe its positive/negative choice but what does that have to do with everything?),
Isn't there any brief and clean explanation you guys could provide me with?
I spent 600 years of trying to understand wikipedia. I failed tremendously.
What is precision?
It refers to how closely a binary floating point representation can represent a real value. Real values have infinite precision and infinite range. Digital values have finite range and precision. In practice a single-precision IEEE-754 can represent real values of a precision of 6 significant figures (decimal), while double-precision is good for 15 significant figures.
The practical effect of this for example is that a single precision value: 123456000.00 cannot be distinguished from say 123456001.00, but equally a value 0.00123456 can be represented.
What is it used for?
Precision is not used for anything other than to define a characteristic of a particular floating point representation.
What is mantissa and what is mantissa used for?
The term is not mentioned in the English language Wikipedia article, and is imprecise - in mathematics in general it has a different meaning that that used here.
The correct term is significand. For a decimal value 0.00123456 for example the significand is is 123456. 123456000.00 has exactly the same significand. Each of these values has the same significand but a different exponent. The exponent is a scaling factor which determines where the decimal point is (hence floating point).
Of course IEEE754 is a binary floating point representation not decimal, but for the same of explanation of the terms it is perhaps easier to use decimal.
How to determine the range of float/double by their size?
By the size alone you cannot; you need to know how many bits are assigned to the significand and how many bits are assigned to the exponent. In C however the range is defined by the macros FLT_MIN, FLT_MAX, DBL_MIN and DBL_MAX in the float.h header. Other characteristics of the implementations floating point representation are described there also.
Note that a specific compiler may not in fact use IEEE754, however that is the format used by most hardware FPU implementations, and the compiler will naturally follow that. For targets with no FPU (small embedded processors typically), other formats may be used.
What is ± symbol (Plus-minus) used for?
It simply means that the value given may be both positive or negative. It may refer to a specific value, or it may indicate a range. So ±n may refer to two discrete values -n or +n, or it may mean a range -n to +n. Context is everything! In this article it refers to discrete values +0, -0, +∞ and -∞.
There are 3 different components: sign, exponent, mantissa
Assuming that the exponent has only 2 Bits, 4 combinations are possible:
binary decimal
00 0
01 1
10 2
11 3
The represented floating-point value is 2exponent:
binary exponent-value
00 2^0 = 1
01 2^1 = 2
10 2^2 = 4
11 2^3 = 8
The range of the floating point value, results from the exponent. 2 bits => maximum value = 8.
The mantissa divide the range from a given exponent to the next higher exponent.
For example the exponent is 2 and the mantissa has one bit, then there are two values possible:
exponent-value mantissa-binary represented floating-point value
2 0 2
2 1 3
The represented floating-point value is 2exponent × (1 + m1×2-1 + m2×2-2 + m3×2-3 + …).
Here an example with a 3 bit mantissa:
exponent-value mantissa-binary represented floating-point value
2 000 2 * (1 ) = 2
2 001 2 * (1 + 2^-3) = 2,25
2 010 2 * (1 + 2^-2 ) = 2,5
2 011 2 * (1 + 2^-2 + 2^-3) = 2,75
2 100 2 * (1 + 2^-1 ) = 3
and so on…
The sign has only just one Bit:
0 -> positive value
1 -> negative value
In IEEE-754 a 32 bit floating-point data type has an 8 bit exponent (with a range from 2-127 to 2128) and a 23 bit mantissa.
1 10000010 01101000000000000000000
- 130 1,40625
The represented floating-point value for this is:
-1 × 2(130 – 127) × (1 + 2-2 + 2-3 + 2-5) = -11,25
Try it: http://www.h-schmidt.net/FloatConverter/IEEE754.html