You hand the pack in indexed, I use colormap, if some of your colors are not from painting you will be warned and if not fixed you will be disqualified.
Resized the terrain.png (Now I know that I have to set interpolation to none.)
Added the 32x dirt block onto the original one. (In an added layer, did that screw it up?)
And that's pretty much it... (Of course, I added the terrain.png in the texture pack folder.)
So I'm guessing I just set the interpolation to none and then it's fixed? Seems simple enough...
By the way, I apologize for so many questions, this is my first block EVER so... I'm a bit of a noob... But this has been a really great opportunity to get better, especially with so many amazing artists here! Yeah, so... Thanks!!
No, you have to have it all in one layer.
It's the terrain.PNG, which can only have 1 layer. (I believe)
This is exactly why you must only use GIMP and not that other program.
Also when you use GIMP you don't SAVE, you EXPORT to Portable Net Graphics.
The only thing Interpolation=None does is make it so when you scale the default PNG up it wont be blurred.
Grass is done, but I have no idea what I should do to the dirt. My browns are a few shades apart and it looks awful if I try to use a cobblestone-like texture... Any tips/suggestions?
Grass is done, but I have no idea what I should do to the dirt. My browns are a few shades apart and it looks awful if I try to use a cobblestone-like texture... Any tips/suggestions?
Grass is done, but I have no idea what I should do to the dirt. My browns are a few shades apart and it looks awful if I try to use a cobblestone-like texture... Any tips/suggestions?
Cool grass, I would add more texture.
Unless you want to make it more of a simplistic styled pack, which is fine.
By texture I mean like actual tufts or blades of grass.
If you want different browns, just pick different browns.
(They have to be from the same painting though :P)
Idk what you use, but in CS2 there is no pencil tool
EDIT: oh wait... Fuuuuuuuuuu it is there when i right click the brush tool.
And it has been there all the time...
fu ck. ._.
Rofl!
Hmm maybe you should try the horizontal layers pattern
Nice grass
Horizontal layers pattern? What is that?
And thanks, it's my first block, I'm excited now!
Cool grass, I would add more texture.
Unless you want to make it more of a simplistic styled pack, which is fine.
By texture I mean like actual tufts or blades of grass.
If you want different browns, just pick different browns.
(They have to be from the same painting though )
Alright, I'll add more texture.
But the painting doesn't have many browns at all. I only found 3 good ones.
Ah I see, I guess I misread the rules. My apologies.
But that's ok, I'm just working on dirt and stone, so I'll just replace the colours.
I'll use aquaxo12's picture if that's ok, it has a similar colour palette that I started using
Grass is done, but I have no idea what I should do to the dirt. My browns are a few shades apart and it looks awful if I try to use a cobblestone-like texture... Any tips/suggestions?
I'm having pretty much the same problem! I cannot find a single good color palette for dirt from my picture! Unless I want baseball-field-like dirt...
Agreed, I might switch if it's allowed. I thought it'd be the best for the terrain but that assumption came back to bite me. I don't know what picture I'd switch to though. It's times like these when I wish I could've used my painting. But, hey, I'll get through it. Which painting looks best to you, Fish?
Agreed, I might switch if it's allowed. I thought it'd be the best for the terrain but that assumption came back to bite me. I don't know what picture I'd switch to though. It's times like these when I wish I could've used my painting. But, hey, I'll get through it. Which painting looks best to you, Fish?
Agreed, I might switch if it's allowed. I thought it'd be the best for the terrain but that assumption came back to bite me. I don't know what picture I'd switch to though. It's times like these when I wish I could've used my painting. But, hey, I'll get through it. Which painting looks best to you, Fish?
I wish I could switch, but I'm using the whole "Alien" thing as my style. I'm sure I'll find some browns somewhere
Alright, my dirt and grass is (finally) done. It might look really weird but I have a weird theme in mind. It looks very chunky and shiny, which I think fits the theme I might be using. (I won't say the theme yet, it's a surprise!)
Alright, my dirt and grass is (finally) done. It might look really weird but I have a weird theme in mind. It looks very chunky and shiny, which I think fits the theme I might be using. (I won't say the theme yet, it's a surprise!)
Here it is tiled:
I would really up the contrast on those greens, and do a little undershading on the dirt under the grass.
Cool though, but also try and dither the big chunks on the dirt.
I would really up the contrast on those greens, and do a little undershading on the dirt under the grass.
Cool though, but also try and dither the big chunks on the dirt.
Erm, how do I dither something? Sorry, as I said, I've never done this before. And I feel really... Weird asking about this because I feel like the answer will be fairly obvious. But, it might not be. Anyways, how do you dither something?
Erm, how do I dither something? Sorry, as I said, I've never done this before. And I feel really... Weird asking about this because I feel like the answer will be fairly obvious. But, it might not be. Anyways, how do you dither something?
It's not obvious, don't worry.
Also you could have searched it up:
(Wikipedia)
PLEASE HELP
Clos
Dither
From Wikipedia, the free encyclopedia
For other uses, see Dither (disambiguation). Dither is an intentionally applied form of noise used to randomize quantization error, preventing large-scale patterns such as color banding in images. Dither is routinely used in processing of both digital audio and digital video data, and is often one of the last stages of audio production tocompact disc.
The first dictionary definition of "didder," fromThomas Blount, Glossographia Anglicana Nova: Or, A Dictionary, Interpreting Such Hard Words of whatever Language, as are at present used in the English Tongue, with their Etymologies, Definitions, &c. Also, The Terms of Divinity, Law, Physick, Mathematics, History, Agriculture, Logick, Metaphysicks, Grammar, Poetry, Musick, Heraldry, Architecture, Painting, War, and all other Arts and Sciences are herein explain'd, from the best Modern Authors, as, Sir Isaac Newton, Dr. Harris, Dr. Gregory, Mr. Lock, Mr. Evelyn, Mr. Dryden, Mr. Blunt, &c., London, 1707.
…one of the earliest [applications] of dither came in World War II. Airplane bombers used mechanical computers to perform navigation and bomb trajectory calculations. Curiously, these computers (boxes filled with hundreds of gears and cogs) performed more accurately when flying on board the aircraft, and less well on ground. Engineers realized that the vibration from the aircraft reduced the error from sticky moving parts. Instead of moving in short jerks, they moved more continuously. Small vibrating motors were built into the computers, and their vibration was called dither from the Middle English verb "didderen," meaning "to tremble." Today, when you tap a mechanical meter to increase its accuracy, you are applying dither, and modern dictionaries define dither as a highly nervous, confused, or agitated state. In minute quantities, dither successfully makes a digitization system a little more analog in the good sense of the word.
—Ken Pohlmann, Principles of Digital Audio[1]
The term "dither" was published in books on analog computation and hydraulically controlled guns shortly after the war.[2][3] The concept of dithering to reduce quantization patterns was first applied by Lawrence G. Roberts[4] in his 1961 MITmaster's thesis[5] and 1962 article[6] though he did not use the term dither. By 1964 dither was being used in the modern sense described in this article.[7] [edit]In digital processing and waveform analysis
Dither is often used in digital audio and video processing, where it is applied to bit-depth transitions; it is utilized in many different fields where digital processing and analysis are used — especially waveform analysis. These uses include systems using digital signal processing, such as digital audio, digital video, digital photography, seismology, RADAR, weather forecasting systems and many more.
The premise is that quantization and re-quantization of digital data yields error. If that error is repeating and correlated to the signal, the error that results is repeating, cyclical, and mathematically determinable. In some fields, especially where the receptor is sensitive to such artifacts, cyclical errors yield undesirable artifacts. In these fields dither results in less determinable artifacts. The field of audio is a primary example of this — the human ear functions much like a Fourier transform, wherein it hears individual frequencies (for details see Mathematics of hearing). The ear is therefore very sensitive to distortion, or additional frequency content that "colors" the sound differently, but far less sensitive to random noise at all frequencies.[citation needed] [edit]Digital audio
In audio, dither can be useful to break up periodic limit cycles, which are a common problem in digital filters. Random noise is typically less objectionable than the harmonic tones produced by limit cycles.
In 1987, Lipz and Vanderkooy pointed out that different noise types, with different probability density functions, behave differently when used as dither signals, and suggested optimal levels of dither signals for audio.[8][9]
In an analog system, the signal is continuous, but in a PCM digital system, the amplitude of the signal out of the digital system is limited to one of a set of fixed values or numbers. This process is called quantization. Each coded value is a discrete step... if a signal is quantized without using dither, there will be quantization distortion related to the original input signal... In order to prevent this, the signal is "dithered", a process that mathematically removes the harmonics or other highly undesirable distortions entirely, and that replaces it with a constant, fixed noise level.[10]
The final version of audio that goes onto a compact disc contains only 16 bits per sample, but throughout the production process a greater number of bits are typically used to represent the sample. In the end, the digital data must be reduced to 16 bits for pressing onto a CD and distributing.
There are multiple ways to do this. One can, for example, simply discard the excess bits — called truncation. One can also round the excess bits to the nearest value. Each of these methods, however, results in predictable and determinable errors in the result. Take, for example, a waveform that consists of the following values: 1 2 3 4 5 6 7 8
If we reduce our waveform by, say, 20% then we end up with the following values: 0.8 1.6 2.4 3.2 4.0 4.8 5.6 6.4
If we truncate these values we end up with the following data: 0 1 2 3 4 4 5 6
If we instead round these values we end up with the following data: 1 2 2 3 4 5 6 6
For any original waveform, the process of reducing the waveform amplitude by 20% results in regular errors. Take for example a sine wave that, for some portion, matches the values above. Every time the sine wave's value hit "3.2," the truncated result would be off by 0.2, as in the sample data above. Every time the sine wave's value hit "4.0," there would be no error since the truncated result would be off by 0.0, also shown above. The magnitude of this error changes regularly and repeatedly throughout the sine wave's cycle. It is precisely this error which manifests itself asdistortion. What the ear hears as distortion is the additional content at discrete frequencies created by the regular and repeated quantization error.
A plausible solution would be to take the 2 digit number (say, 4.8) and round it one direction or the other. For example, we could round it to 5 one time and then 4 the next time. This would make the long-term average 4.5 instead of 4, so that over the long-term the value is closer to its actual value. This, on the other hand, still results in determinable (though more complicated) error. Every other time the value 4.8 comes up the result is an error of 0.2, and the other times it is –0.8. This still results in a repeating, quantifiable error.
Another plausible solution would be to take 4.8 and round it so that the first four times out of five it rounded up to 5, and the fifth time it rounded to 4. This would average out to exactly 4.8 over the long term. Unfortunately, however, it still results in repeatable and determinable errors, and those errors still manifest themselves as distortion to the ear (though oversampling can reduce this).
This leads to the dither solution. Rather than predictably rounding up or down in a repeating pattern, what if we rounded up or down in a random pattern? If we came up with a way to randomly toggle our results between 4 and 5 so that 80% of the time it ended up on 5 then we would average 4.8 over the long run but would have random, non-repeating error in the result. This is done through dither.
We calculate a series of random numbers between 0.0 and 0.9 (ex: 0.6, 0.1, 0.3, 0.6, 0.9, etc.) and we add these random numbers to the results of our equation. Two times out of ten the result will truncate back to 4 (if 0.0 or 0.1 are added to 4.8) and the rest of the times it will truncate to 5, but each given situation has a random 20% chance of rounding to 4 or 80% chance of rounding to 5. Over the long haul this will result in results that average to 4.8 and a quantization error that is random — or noise. This "noise" result is less offensive to the ear than the determinable distortion that would result otherwise.
0:00 Problems listening to these files? See media help. [edit]Usage
Dither should be added to any low-amplitude or highly-periodic signal before any quantization or re-quantization process, in order to de-correlate the quantization noise from the input signal and to prevent non-linear behavior (distortion); the lesser the bit depth, the greater the dither must be. The result of the process still yields distortion, but the distortion is of a random nature so the resulting noise is, effectively, de-correlated from the intended signal. Any bit-reduction process should add dither to the waveform before the reduction is performed. [edit]Different types
RPDF stands for "Rectangular Probability Density Function," equivalent to a roll of a dice. Any number has the same random probability of surfacing. TPDF stands for "Triangular Probability Density Function," equivalent to a roll of two dice (the sum of two independent samples of RPDF). Gaussian PDF is equivalent to a roll of a large number of dice. The relationship of probabilities of results follows a bell-shaped, or Gaussian curve, typical of dither generated by analog sources such as microphone preamplifiers. If the bit depth of a recording is sufficiently great, that preamp noise will be sufficient to dither the recording. Colored Dither is sometimes mentioned as dither that has been filtered to be different from white noise. Some dither algorithms use noise that has more energy in the higher frequencies so as to lower the energy in the critical audio band. Noise shaping is a filtering process that shapes the spectral energy of quantization error, typically to either de-emphasise frequencies to which the ear is most sensitive or separate the signal and noise bands completely. If dither is used, its final spectrum depends on whether it is added inside or outside the feedback loop of the noise shaper: if inside, the dither is treated as part of the error signal and shaped along with actual quantization error; if outside, the dither is treated as part of the original signal and linearises quantization without being shaped itself. In this case, the final noise floor is the sum of the flat dither spectrum and the shaped quantization noise. While real-world noise shaping usually includes in-loop dithering, it is also possible to use it without adding dither at all, in which case the usual harmonic-distortion effects still appear at low signal levels. [edit]Which types to use
If the signal being dithered is to undergo further processing, then it should be processed with TPDF dither that has an amplitude of two quantization steps (so that the dither values computed range from, say, –1 to +1, or 0 to 2).[9] This is the lowest power ideal dither, in that it does not introduce noise modulation (constant noise floor) and completely eliminates the harmonic distortion from *quantization*. If colored dither is used at these intermediate processing stages then the frequency content can "bleed" into other, more noticeable frequency ranges and become distractingly audible.
If the signal being dithered is to undergo no further processing — it is being dithered to its final result for distribution — then colored dither or noise shaping is appropriate, and can effectively lower the audible noise level by putting most of that noise in a frequency range where it is less critical. [edit]Digital photography and image processing
An illustration of dithering. Red and blue are the only colors used but, as the red and blue squares are made smaller, the patch appears violet.
Dithering is a technique used in computer graphics to create the illusion of color depth in images with a limited color palette (color quantization). In a dithered image, colors not available in the palette are approximated by a diffusion of colored pixels from within the available palette. The human eye perceives the diffusion as a mixture of the colors within it (see color vision). Dithered images, particularly those with relatively few colors, can often be distinguished by a characteristic graininess, or speckled appearance.
By its nature, dithering introduces a pattern in to the image, the idea is that the image is viewed from such a distance the pattern is not discernible to the human eye. Unfortunately this is not typically the case and often the patterning is visible. In these circumstances it has been shown that a blue noise dither pattern is the least unsightly and distracting.[11] The error diffusion techniques were some of the first methods to generate blue noise dithering patterns, however, other techniques such as ordered dithering can also generate blue noise dithering without the tendency to degenerate in to areas with artefacts. [edit]Examples
Reducing the color depth of an image can often have significant visual side-effects. If the original image is a photograph, it is likely to have thousands, or even millions of distinct colors. The process of constraining the available colors to a specific color palette effectively throws away a certain amount of color information.
A number of factors can affect the resulting quality of a color-reduced image. Perhaps most significant is the color palette that will be used in the reduced image. For example, an original image (Figure 1) may be reduced to the 216-color "web-safe" color palette. If the original pixel colors are simply translated into the closest available color from the palette, no dithering occurs (Figure 2). Typically, this approach results in flat areas (contours) and a loss of detail, and may produce patches of color that are significantly different from the original. Shaded or gradient areas may appear as color bands, which may be distracting. The application of dithering can help to minimize such visual artifacts, and usually results in a better representation of the original (Figure 3). Dithering helps to reduce color banding and flatness.
One of the problems associated with using a fixed color palette is that many of the needed colors may not be available in the palette, and many of the available colors may not be needed; a fixed palette containing mostly shades of green would not be well-suited for images that do not contain many shades of green, for instance. The use of an optimized color palette can be of benefit in such cases. An optimized color palette is one in which the available colors are chosen based on how frequently they are used in the original source image. If the image is reduced based on an optimized palette, the result is often much closer to the original (Figure 4).
The number of colors available in the palette is also a contributing factor. If, for example, the palette is limited to only 16 colors, the resulting image could suffer from additional loss of detail, and even more pronounced problems with flatness and color banding (Figure 5). Once again, dithering can help to minimize such artifacts (Figure 6).
Figure 1. Original photo; note the smoothness in the detail.
Figure 2. Original image using the web-safe color palette with no dithering applied. Note the large flat areas and loss of detail.
Figure 3. Original image using the web-safe color palette with Floyd–Steinberg dithering. Note that even though the same palette is used, the application of dithering gives a better representation of the original.
Figure 4. Here, the original has been reduced to a 256-color optimized palette with Floyd–Steinberg dithering applied. The use of an optimized palette, rather than a fixed palette, allows the result to better represent the colors in the original image.
Figure 5. Depth is reduced to a 16-color optimized palette in this image, with no dithering. Colors appear muted, and color banding is pronounced.
Figure 6. This image also uses the 16-color optimized palette, but the use of dithering helps to reduce banding.
I'm just curious, how will you check our colors?
You hand the pack in indexed, I use colormap, if some of your colors are not from painting you will be warned and if not fixed you will be disqualified.
No, you have to have it all in one layer.
It's the terrain.PNG, which can only have 1 layer. (I believe)
This is exactly why you must only use GIMP and not that other program.
Also when you use GIMP you don't SAVE, you EXPORT to Portable Net Graphics.
The only thing Interpolation=None does is make it so when you scale the default PNG up it wont be blurred.
I used to use Photoshop Elements 2.0.
Cool grass, I would add more texture.
Unless you want to make it more of a simplistic styled pack, which is fine.
By texture I mean like actual tufts or blades of grass.
If you want different browns, just pick different browns.
(They have to be from the same painting though :P)
Rofl!
Horizontal layers pattern? What is that?
And thanks, it's my first block, I'm excited now!
Alright, I'll add more texture.
But the painting doesn't have many browns at all. I only found 3 good ones.
Which painting are you using, the one by imnumberfour?
Yes, sir. I found the three good ones in the chimney and the woman walking down the street.
Cool!
lol that painting is a b*tch when it comes to colors other than yellow, green, red, blue.
I'm having pretty much the same problem! I cannot find a single good color palette for dirt from my picture! Unless I want baseball-field-like dirt...
Mine.
Just kidding.
Try this one:
Here's my palette:
I couldn't find a nice, bright yellow. Not even in the rainbow. ):
You better put those browns and greys to use, that'll make for some nice stone and dirt!
Cool pallete!
I wish I could switch, but I'm using the whole "Alien" thing as my style. I'm sure I'll find some browns somewhere
Here it is tiled:
I would really up the contrast on those greens, and do a little undershading on the dirt under the grass.
Cool though, but also try and dither the big chunks on the dirt.
Erm, how do I dither something? Sorry, as I said, I've never done this before. And I feel really... Weird asking about this because I feel like the answer will be fairly obvious. But, it might not be. Anyways, how do you dither something?
It's not obvious, don't worry.
Also you could have searched it up:
(Wikipedia)
Dither
From Wikipedia, the free encyclopedia
For other uses, see Dither (disambiguation).
Dither is an intentionally applied form of noise used to randomize quantization error, preventing large-scale patterns such as color banding in images. Dither is routinely used in processing of both digital audio and digital video data, and is often one of the last stages of audio production tocompact disc.
Contents
[hide]
[edit]Etymology
The first dictionary definition of "didder," fromThomas Blount, Glossographia Anglicana Nova: Or, A Dictionary, Interpreting Such Hard Words of whatever Language, as are at present used in the English Tongue, with their Etymologies, Definitions, &c. Also, The Terms of Divinity, Law, Physick, Mathematics, History, Agriculture, Logick, Metaphysicks, Grammar, Poetry, Musick, Heraldry, Architecture, Painting, War, and all other Arts and Sciences are herein explain'd, from the best Modern Authors, as, Sir Isaac Newton, Dr. Harris, Dr. Gregory, Mr. Lock, Mr. Evelyn, Mr. Dryden, Mr. Blunt, &c., London, 1707.
…one of the earliest [applications] of dither came in World War II. Airplane bombers used mechanical computers to perform navigation and bomb trajectory calculations. Curiously, these computers (boxes filled with hundreds of gears and cogs) performed more accurately when flying on board the aircraft, and less well on ground. Engineers realized that the vibration from the aircraft reduced the error from sticky moving parts. Instead of moving in short jerks, they moved more continuously. Small vibrating motors were built into the computers, and their vibration was called dither from the Middle English verb "didderen," meaning "to tremble." Today, when you tap a mechanical meter to increase its accuracy, you are applying dither, and modern dictionaries define dither as a highly nervous, confused, or agitated state. In minute quantities, dither successfully makes a digitization system a little more analog in the good sense of the word.
—Ken Pohlmann, Principles of Digital Audio[1]
[edit]In digital processing and waveform analysis
Dither is often used in digital audio and video processing, where it is applied to bit-depth transitions; it is utilized in many different fields where digital processing and analysis are used — especially waveform analysis. These uses include systems using digital signal processing, such as digital audio, digital video, digital photography, seismology, RADAR, weather forecasting systems and many more.
The premise is that quantization and re-quantization of digital data yields error. If that error is repeating and correlated to the signal, the error that results is repeating, cyclical, and mathematically determinable. In some fields, especially where the receptor is sensitive to such artifacts, cyclical errors yield undesirable artifacts. In these fields dither results in less determinable artifacts. The field of audio is a primary example of this — the human ear functions much like a Fourier transform, wherein it hears individual frequencies (for details see Mathematics of hearing). The ear is therefore very sensitive to distortion, or additional frequency content that "colors" the sound differently, but far less sensitive to random noise at all frequencies.[citation needed]
[edit]Digital audio
In audio, dither can be useful to break up periodic limit cycles, which are a common problem in digital filters. Random noise is typically less objectionable than the harmonic tones produced by limit cycles.
In 1987, Lipz and Vanderkooy pointed out that different noise types, with different probability density functions, behave differently when used as dither signals, and suggested optimal levels of dither signals for audio.[8][9]
In an analog system, the signal is continuous, but in a PCM digital system, the amplitude of the signal out of the digital system is limited to one of a set of fixed values or numbers. This process is called quantization. Each coded value is a discrete step... if a signal is quantized without using dither, there will be quantization distortion related to the original input signal... In order to prevent this, the signal is "dithered", a process that mathematically removes the harmonics or other highly undesirable distortions entirely, and that replaces it with a constant, fixed noise level.[10]
The final version of audio that goes onto a compact disc contains only 16 bits per sample, but throughout the production process a greater number of bits are typically used to represent the sample. In the end, the digital data must be reduced to 16 bits for pressing onto a CD and distributing.
There are multiple ways to do this. One can, for example, simply discard the excess bits — called truncation. One can also round the excess bits to the nearest value. Each of these methods, however, results in predictable and determinable errors in the result. Take, for example, a waveform that consists of the following values: 1 2 3 4 5 6 7 8
If we reduce our waveform by, say, 20% then we end up with the following values: 0.8 1.6 2.4 3.2 4.0 4.8 5.6 6.4
If we truncate these values we end up with the following data: 0 1 2 3 4 4 5 6
If we instead round these values we end up with the following data: 1 2 2 3 4 5 6 6
For any original waveform, the process of reducing the waveform amplitude by 20% results in regular errors. Take for example a sine wave that, for some portion, matches the values above. Every time the sine wave's value hit "3.2," the truncated result would be off by 0.2, as in the sample data above. Every time the sine wave's value hit "4.0," there would be no error since the truncated result would be off by 0.0, also shown above. The magnitude of this error changes regularly and repeatedly throughout the sine wave's cycle. It is precisely this error which manifests itself asdistortion. What the ear hears as distortion is the additional content at discrete frequencies created by the regular and repeated quantization error.
A plausible solution would be to take the 2 digit number (say, 4.8) and round it one direction or the other. For example, we could round it to 5 one time and then 4 the next time. This would make the long-term average 4.5 instead of 4, so that over the long-term the value is closer to its actual value. This, on the other hand, still results in determinable (though more complicated) error. Every other time the value 4.8 comes up the result is an error of 0.2, and the other times it is –0.8. This still results in a repeating, quantifiable error.
Another plausible solution would be to take 4.8 and round it so that the first four times out of five it rounded up to 5, and the fifth time it rounded to 4. This would average out to exactly 4.8 over the long term. Unfortunately, however, it still results in repeatable and determinable errors, and those errors still manifest themselves as distortion to the ear (though oversampling can reduce this).
This leads to the dither solution. Rather than predictably rounding up or down in a repeating pattern, what if we rounded up or down in a random pattern? If we came up with a way to randomly toggle our results between 4 and 5 so that 80% of the time it ended up on 5 then we would average 4.8 over the long run but would have random, non-repeating error in the result. This is done through dither.
We calculate a series of random numbers between 0.0 and 0.9 (ex: 0.6, 0.1, 0.3, 0.6, 0.9, etc.) and we add these random numbers to the results of our equation. Two times out of ten the result will truncate back to 4 (if 0.0 or 0.1 are added to 4.8) and the rest of the times it will truncate to 5, but each given situation has a random 20% chance of rounding to 4 or 80% chance of rounding to 5. Over the long haul this will result in results that average to 4.8 and a quantization error that is random — or noise. This "noise" result is less offensive to the ear than the determinable distortion that would result otherwise.
Audio samples:
16-bit sine wave
0:00
truncated to 6 bits
0:00
Problems listening to these files? See media help.
[edit]Usage
Dither should be added to any low-amplitude or highly-periodic signal before any quantization or re-quantization process, in order to de-correlate the quantization noise from the input signal and to prevent non-linear behavior (distortion); the lesser the bit depth, the greater the dither must be. The result of the process still yields distortion, but the distortion is of a random nature so the resulting noise is, effectively, de-correlated from the intended signal. Any bit-reduction process should add dither to the waveform before the reduction is performed.
[edit]Different types
RPDF stands for "Rectangular Probability Density Function," equivalent to a roll of a dice. Any number has the same random probability of surfacing.
TPDF stands for "Triangular Probability Density Function," equivalent to a roll of two dice (the sum of two independent samples of RPDF).
Gaussian PDF is equivalent to a roll of a large number of dice. The relationship of probabilities of results follows a bell-shaped, or Gaussian curve, typical of dither generated by analog sources such as microphone preamplifiers. If the bit depth of a recording is sufficiently great, that preamp noise will be sufficient to dither the recording.
Colored Dither is sometimes mentioned as dither that has been filtered to be different from white noise. Some dither algorithms use noise that has more energy in the higher frequencies so as to lower the energy in the critical audio band.
Noise shaping is a filtering process that shapes the spectral energy of quantization error, typically to either de-emphasise frequencies to which the ear is most sensitive or separate the signal and noise bands completely. If dither is used, its final spectrum depends on whether it is added inside or outside the feedback loop of the noise shaper: if inside, the dither is treated as part of the error signal and shaped along with actual quantization error; if outside, the dither is treated as part of the original signal and linearises quantization without being shaped itself. In this case, the final noise floor is the sum of the flat dither spectrum and the shaped quantization noise. While real-world noise shaping usually includes in-loop dithering, it is also possible to use it without adding dither at all, in which case the usual harmonic-distortion effects still appear at low signal levels.
[edit]Which types to use
If the signal being dithered is to undergo further processing, then it should be processed with TPDF dither that has an amplitude of two quantization steps (so that the dither values computed range from, say, –1 to +1, or 0 to 2).[9] This is the lowest power ideal dither, in that it does not introduce noise modulation (constant noise floor) and completely eliminates the harmonic distortion from *quantization*. If colored dither is used at these intermediate processing stages then the frequency content can "bleed" into other, more noticeable frequency ranges and become distractingly audible.
If the signal being dithered is to undergo no further processing — it is being dithered to its final result for distribution — then colored dither or noise shaping is appropriate, and can effectively lower the audible noise level by putting most of that noise in a frequency range where it is less critical.
[edit]Digital photography and image processing
256 color dithering with Irfan View
Dithering is a technique used in computer graphics to create the illusion of color depth in images with a limited color palette (color quantization). In a dithered image, colors not available in the palette are approximated by a diffusion of colored pixels from within the available palette. The human eye perceives the diffusion as a mixture of the colors within it (see color vision). Dithered images, particularly those with relatively few colors, can often be distinguished by a characteristic graininess, or speckled appearance.
By its nature, dithering introduces a pattern in to the image, the idea is that the image is viewed from such a distance the pattern is not discernible to the human eye. Unfortunately this is not typically the case and often the patterning is visible. In these circumstances it has been shown that a blue noise dither pattern is the least unsightly and distracting.[11] The error diffusion techniques were some of the first methods to generate blue noise dithering patterns, however, other techniques such as ordered dithering can also generate blue noise dithering without the tendency to degenerate in to areas with artefacts.
[edit]Examples
Reducing the color depth of an image can often have significant visual side-effects. If the original image is a photograph, it is likely to have thousands, or even millions of distinct colors. The process of constraining the available colors to a specific color palette effectively throws away a certain amount of color information.
A number of factors can affect the resulting quality of a color-reduced image. Perhaps most significant is the color palette that will be used in the reduced image. For example, an original image (Figure 1) may be reduced to the 216-color "web-safe" color palette. If the original pixel colors are simply translated into the closest available color from the palette, no dithering occurs (Figure 2). Typically, this approach results in flat areas (contours) and a loss of detail, and may produce patches of color that are significantly different from the original. Shaded or gradient areas may appear as color bands, which may be distracting. The application of dithering can help to minimize such visual artifacts, and usually results in a better representation of the original (Figure 3). Dithering helps to reduce color banding and flatness.
One of the problems associated with using a fixed color palette is that many of the needed colors may not be available in the palette, and many of the available colors may not be needed; a fixed palette containing mostly shades of green would not be well-suited for images that do not contain many shades of green, for instance. The use of an optimized color palette can be of benefit in such cases. An optimized color palette is one in which the available colors are chosen based on how frequently they are used in the original source image. If the image is reduced based on an optimized palette, the result is often much closer to the original (Figure 4).
The number of colors available in the palette is also a contributing factor. If, for example, the palette is limited to only 16 colors, the resulting image could suffer from additional loss of detail, and even more pronounced problems with flatness and color banding (Figure 5). Once again, dithering can help to minimize such artifacts (Figure 6).
Figure 1. Original photo; note the smoothness in the detail.
Figure 2. Original image using the web-safe color palette with no dithering applied. Note the large flat areas and loss of detail.
Figure 3. Original image using the web-safe color palette with Floyd–Steinberg dithering. Note that even though the same palette is used, the application of dithering gives a better representation of the original.
Figure 4. Here, the original has been reduced to a 256-color optimized palette with Floyd–Steinberg dithering applied. The use of an optimized palette, rather than a fixed palette, allows the result to better represent the colors in the original image.
Figure 5. Depth is reduced to a 16-color optimized palette in this image, with no dithering. Colors appear muted, and color banding is pronounced.
Figure 6. This image also uses the 16-color optimized palette, but the use of dithering helps to reduce banding.
Spriteland.com
http://www.spritelan...ng-tutorial.htm
Practically it's just noise to create a smoother look.
But sometimes it looks rougher, aka when you shouldn't use it.
Or maybe you should.