线性量化和非线性量化有什么区别？
线性量化和非线性量化有什么区别？我说的是 PCM 样本。 http://www.blurtit.com/q927781.html 有一篇关于它的文章，但我'我正在寻找更详细的答案。
What is the difference between Linear Quantization and Non-linear Quantization ? I'm talking with regard to PCM samples.
http://www.blurtit.com/q927781.html has an article about it but I'm looking for a more elaborate answer.
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这真的很简单。通过线性量化，采样值中的每个增量对应于固定大小的模拟增量。例如，模拟范围为 0 - 1 V 的 8 位 AD 或 DA 每位的电压为 1 / 256 = 3.9 mV，无论实际信号幅度如何。
对于非线性量化，您通常有某种对数编码（例如 μ-Law 或 A-law），因此小样本值的增量远小于大样本值的增量。理想情况下，步长应大致与样本大小成比例。这转化为固定的信噪比（由于量化噪声），无论信号幅度如何。另一种看待这个问题的方法是，您可以使用更少的位数在感兴趣的信号幅度范围内获得给定的信噪比。
It's pretty simple really. With linear quantization every increment in the sampled value corresponds to a fixed size analogue increment. E.g. an 8 bit A-D or D-A with a 0 - 1 V analogue range has 1 / 256 = 3.9 mV per bit, regardless of the actual signal amplitude.
With non-linear quantization you normally have some sort of logarithmic encoding (e.g. µ-Law or A-law), so that the increment for small sample values is much smaller than the increment for large sample values. Ideally the step size should be roughly proportional to the sample size. This translates to a fixed S/N ratio (due to quantization noise), regardless of the signal amplitude. Another way of looking at this is that you can use fewer bits to get a given S/N ratio over the signal amplitude range of interest.
除了样本大小与决策水平的中点完全一致的情况外，每个样本都会出现错误。
如果采取较小的步长，量化误差将会较小。然而，增加步骤将使编码操作复杂化并增加带宽要求。
量化噪声取决于步长而不是信号幅度。
量化间隔大小相等。
量化噪声：输入信号与量化输出信号之间的差异
信噪比
信噪比= 6n+1.76 dBn=8，S/N=49.76 dB换句话说，每添加一个二进制数字，比率就会增加
6dBs示例
考虑示例
2，信号的实际幅度为+1.7V。这被指定为电平
2（对于 1 和 2 之间的任何电压都相同），以线路代码101的形式传输。在接收端将
101转换为+1.5V的脉冲（编码器处判决电平的中间值）这会在原始输入和输出信号之间产生
0.2V的误差。非线性量化
通过线性量化，高电平信号的信噪比较大，但低电平信号的信噪比较小。
因此，使用非线性量化。
量化间隔的大小不相等。
小量化间隔被分配给小信号值（样本），大量化间隔被分配给大样本，使得信号量化失真比几乎与信号电平无关。
弱信号的信噪比要好得多，但强信号的信噪比稍低。
命令：先压缩后扩展的过程。
使用两个单独的法律
A-Law 被 ITU-T 采用用于 30 通道 PCM。
µ-law 主要用于美国、加拿大和日本。
Errors occur on every sample except where the sample size exactly coincides the mid-point of the decision level.
If smaller steps are taken the quantization error will be less. However, increasing the steps will complicate the coding operation and increase bandwidth requirements.
Quantizing noise depends on step size and not on signal amplitude
The quantizing intervals are of equal size.
Quantization noise: the difference between the input signal and the quantized output signal
Signal-to-quantized-noise ratio
S/N= 6n+1.76 dBn=8,S/N=49.76 dBIn other words, every added binary digit increases the ratio by
6dBsExample
Consider sample
2, the actual amplitude of the signal is+1.7V.This is assigned level
2(same for any voltage between 1 & 2), which is transmitted as line code101.At the receiving end
101is converted to a pulse of+1.5V(the middle value of the decision level at the encoder)This produces an error of
0.2Vbetween original input and output signals.Non-Linear Quantization
With linear quantization, the signal to noise ratio is large for high levels but small for low level signals.
Therefore, non-linear quantization is used.
The quantizing intervals are not of equal size.
Small quantizing intervals are allocated to small signal values (samples) and large quantization intervals to large samples so that the signal-to-quantization distortion ratio is nearly independent of the signal level.
S/N ratios for weak signals are much better but are slightly less for the stronger signals.
Commanding: a process in which compression is followed by expansion.
Two separate laws are used
A-Law adopted by ITU-T for 30 channel PCM.
µ-law used mainly in USA,Canada and Japan.