In the first step, the block quantizes an input value to one of 2B uniformly spaced levels in the range [-V, (1-21-B)V], where you specify B in the Bits parameter and you specify V in the Peak parameter. The quantization process rounds both positive and negative inputs downward to the nearest quantization level, with the exception of those that fall exactly on a quantization boundary. The real and imaginary components of complex inputs are quantized independently.
Because the set of possible output values of a quantizer is countable, any quantizer can be decomposed into two distinct stages, which can be referred to as the classification stage (or forward quantization stage) and the reconstruction stage (or inverse quantization stage), where the classification stage maps the input value to an integer quantization index k \displaystyle k and the reconstruction stage maps the index k \displaystyle k to the reconstruction value y k \displaystyle y_k that is the output approximation of the input value. For the example uniform quantizer described above, the forward quantization stage can be expressed as
Matlab program for uniform quantization encoding
A dead-zone quantizer is a type of mid-tread quantizer with symmetric behavior around 0. The region around the zero output value of such a quantizer is referred to as the dead zone or deadband. The dead zone can sometimes serve the same purpose as a noise gate or squelch function. Especially for compression applications, the dead-zone may be given a different width than that for the other steps. For an otherwise-uniform quantizer, the dead-zone width can be set to any value w \displaystyle w by using the forward quantization rule[10][11][12]
Here, the quantization noise is once again assumed to be uniformly distributed. When the input signal has a high amplitude and a wide frequency spectrum this is the case.[16] In this case a 16-bit ADC has a maximum signal-to-noise ratio of 98.09 dB. The 1.761 difference in signal-to-noise only occurs due to the signal being a full-scale sine wave instead of a triangle or sawtooth.
For complex signals in high-resolution ADCs this is an accurate model. For low-resolution ADCs, low-level signals in high-resolution ADCs, and for simple waveforms the quantization noise is not uniformly distributed, making this model inaccurate.[17] In these cases the quantization noise distribution is strongly affected by the exact amplitude of the signal.
or approximately 6 dB per bit. For example, for N \displaystyle N =8 bits, M \displaystyle M =256 levels and SQNR = 86 = 48 dB; and for N \displaystyle N =16 bits, M \displaystyle M =65536 and SQNR = 166 = 96 dB. The property of 6 dB improvement in SQNR for each extra bit used in quantization is a well-known figure of merit. However, it must be used with care: this derivation is only for a uniform quantizer applied to a uniform source. For other source PDFs and other quantizer designs, the SQNR may be somewhat different from that predicted by 6 dB/bit, depending on the type of PDF, the type of source, the type of quantizer, and the bit rate range of operation.
I'm trying to quantize a set of double type samples with 128 level uniform quantizer and I want my output to be double type aswell. When I try to use "quantize" matlab gives an error: Inputs of class 'double' are not supported. I tried "uencode" as well but its answer was nonsense. I'm quite new to matlab and I've been working on this for hours. Any help appriciated. Thanks
According to the Barlow hypothesis , the perceptualrepresentation of the image should also be statistically efficient. Thematch with natural image statisticsand the fact that most of the image coding applications are intended tobe judged by a human observer have motivated the use of human vision models and perceptual metrics to inspirethe image representation in transform coders as well asthe bit allocation in the selected representation (e.g. JPEG andJPEG2000). Ourworkinthis field has been focused in using accurate models of theperceptual non-linearties to improve these standards. The key issue ismaking a uniformquantization in a perceptually uniform domain. We have developed adistortioncriterion that unifies all the results in perceptually basedquantization:making a uniform quantization in the perceptual domain is equivalentto restrict the Maximum Perceptual Error (MPE) in each component of theperceptual representation. When using this concept, all the proposedapproaches (ours and those of other people, e.g. JPEG and JPEG2000)are just particular cases using different perception models. 2ff7e9595c
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