2. ⇢ 1: Vector Quantization can
lower the average distortion
with the number of
reconstruction levels held
constant, While Scalar
Quantization cannot.
⇢ 2: Vector Quantization can
reduce the number of
reconstruction levels when
distortion is held constant,
While Scalar Quantization
cannot.
3. ⇢ 3: The most significant way
Vector Quantization can
improve performance over
Scalar Quantization is by
exploiting the statistical
dependence among scalars in
the block.
⇢ 4: Vector Quantization is also
more effective than Scalar
Quantization When the source
output values are not
correlated.
4. ⇢ 5: In Scalar Quantization, in One
Dimension, the quantization
regions are restricted to be in
intervals(i.e., Output points are
restricted to be rectangular grids)
and the only parameter we can
manipulate is the size of the
interval. While, in Vector
Quantization, When we divide the
input into vectors of some length
n, the quantization regions are no
longer restricted to be rectangles
or squares, we have the freedom
to divide the range of the inputs in
an infinite number of ways.
⇢ 6: In Scalar Quantization, the
Granular Error is affected by
size of quantization interval
only, while in Vector
Quantization, Granular Error
is affected by the both shape
and size of quantization
interval.
5. ⇢ 7: Vector Quantization
provides more flexibility
towards modifications than
Scalar Quantization. The
flexibility of Vector
Quantization towards
modification increases with
increasing dimension.
⇢ 8: Vector Quantization have
improved performance when
there is sample to sample
dependence of input, While
not in Scalar Quantization.
6. ⇢ 9: Vector Quantization have
improved performance when
there is not the sample to
sample dependence of input,
While not in Scalar
Quantization.
⇢ 10:Describing the decision
boundaries between
reconstruction levels is easier
in Scalar Quatization than in
Vector Quatization.
