## What is meant by adaptive quantization?

In forward adaptive quantization, the source output is divided into blocks of data. Each block is analyzed before quantization, and the quantizer parameters are set accordingly. The settings of the quantizer are then transmitted to the receiver as side information.

**What characterizes a quantizer?**

A quantizer maps an input amplitude to an output amplitude, and the output amplitude takes on one of N allowed values. A good quantizer has a small error term, and a poor quantizer has a large error term.

**What is block adaptive quantization?**

The block adaptive quantization (BAQ) was created by Kwok and Johnson of the NASA JPL (Kwok and Johnson, 1989) . The BAQ agorithm is a non-uniform quantizer improved for a Gaussian probability distribution where the threshold values are adjusted on a block by block basis, and are obtained from the block variance.

### Why do we require vector quantization?

Vector quantization is used for lossy data compression, lossy data correction, pattern recognition, density estimation and clustering. Lossy data correction, or prediction, is used to recover data missing from some dimensions.

**What is the advantage of using vector quantization?**

1. Vector Quantization can lower the average distortion D with number of reconstruction levels held constant; or, 2. Can reduce the number of reconstruction levels when D is held constant.

**What is the output of quantizer?**

When the first input is received, the quantizer step size is 0.5. Therefore, the input falls into level 0, and the output value is 0.25, resulting in an error of 0.15. As this input fell into quantizer level 0, the new step size is M 0 × Δ 0 = 0.8 × 0.5 = 0.4 .

## What is the difference between the quantizer and the encoder?

Quantization: The process of transforming the continuous amplitude samples x(n𝒕𝒔) into a discrete amplitude samples 𝒙𝒒 (n𝒕𝒔) taken from a finite set of possible levels. Encoding: it’s converts each quantized sample 𝒙_𝒒 “(n” 𝒕_𝒔) into “ b ” bits codeword.

**Which coding scheme that takes advantage of long runs of identical symbols?**

RLE is a simple lossless compression technique that assigns short codes to long runs of identical symbols.

**What are disadvantages of quantization?**

What is the disadvantage of uniform quantization over the non-uniform quantization? SNR decreases with decrease in input power level at the uniform quantizer but non-uniform quantization maintains a constant SNR for wide range of input power levels.

### Why do we use quantization?

Quantization introduces various sources of error in your algorithm, such as rounding errors, underflow or overflow, computational noise, and limit cycles. This results in numerical differences between the ideal system behavior and the computed numerical behavior.

**Why is vector quantization rarely used in practical applications?**

(12) Why is vector quantization rarely used in practical applications? It requires block Huffman coding of quantization indexes, which is very complex. c. The computational complexity, in particular for the encoding, is much higher than in scalar quantization and a large codebook needs to be stored.

**How are the parameters set in adaptive quantization?**

In forward adaptive quantization, the source output is divided into blocks of data. Each block is analyzed before quantization, and the quantizer parameters are set accordingly. The settings of the quantizer are then transmitted to the receiver as side information.

## Why do we need to feed backward adaptive quantization?

Feed Backward Adaptive Quantization • There is no need to send side information. • The sensitivity of adaptation to the changing statistics will be degraded, however, since instead of the original input, only the output of the quantization encoder used in the statistical in the statistical analysis.

**How is the Jayant method of adaptive quantization represented?**

Jayant named this quantization approach “quantization with one word memory.” The quantizer is better known as the Jayant quantizer. Mathematically, the adaptation process can be represented as where l (n−1) is the quantization interval at time (n−1).

**How to feed a feed forward quantizer to a decoder?**

Feed Forward Quantizer • Time varying gain is G [n],c [n] and G [n] need to be transmitted to the decoder.