assigning discrete integer values to PAM sample inputs
Encoding the sign and magnitude of a quantization interval as binary digits
Quantization range refers to the range of values that can be represented by a quantization process. In digital signal processing, quantization is the process of mapping input values to a discrete set of output values. The quantization range determines the precision and accuracy of the quantization process.
Quantization can be broadly categorized into two main types: uniform and non-uniform quantization. Uniform quantization divides the input range into equal-sized intervals, making it simple and efficient for certain applications. Non-uniform quantization, on the other hand, allocates varying interval sizes, often used in scenarios where certain ranges of input values are more significant, such as in audio compression. Additionally, there are techniques like scalar quantization and vector quantization, which refer to the quantization of individual signals versus groups of signals, respectively.
one syllable LOL
The ideal Quantization error is 2^N/Analog Voltage
Sampling Discritizes in time Quantization discritizes in amplitude
Mid riser quantization is a type of quantization scheme used in analog-to-digital conversion where the input signal range is divided into equal intervals, with the quantization levels located at the midpoints of these intervals. This approach helps reduce quantization error by evenly distributing the error across the positive and negative parts of the signal range.
There are two types of quantization .They are, 1. Truncation. 2.Round off.
Compressing a voice signal sample into segments prior to quantization and expanding it it its original size once transmitted Compressing larger signals more than smaller signals
Quantization noise is a model of quantization error introduced by quantization in the analog-to-digital conversion(ADC) in telecommunication systems and signal processing.
Non-linear quantization is a method of quantizing signals where the quantization levels are not evenly spaced. Instead, it allocates more quantization levels to regions of interest or higher signal variability, allowing for better representation of the signal's nuances and reducing distortion in those areas. This approach is commonly used in audio and image compression to improve perceptual quality while minimizing data size. By adapting the quantization process to the characteristics of the signal, non-linear quantization can enhance performance compared to linear methods.
quantisation noise decrease and quantization density remain same.
Quantization refers to the process of constraining an input from a large set to output in a smaller set, often in the context of digital signal processing. The number of quantization levels determines how many discrete values a continuous signal can take, which directly impacts the resolution and accuracy of the representation. For example, in an 8-bit quantization, there are 256 (2^8) possible levels. The choice of quantization levels is crucial for balancing fidelity and data size.