Image Processing Reference

In-Depth Information

(1)

where
α
(
ν
,
r
) is the e-CSF weighting function that tries to reproduce some perceptual proper-

ties of the HVS. The term
α
(
ν
,
r
)
ω
s
,
o
is considered the
perceptual wavelet coefficients
of image
I

when observed at distance
d
.

(2)
Forward quantization
(
F
-pSQ): Quantization is the only cause that introduces distortion

mapped independently to a corresponding step size either
Δ
s
or
Δ
n
, thus
I
ρ
is associated

with a specific interval on the real line. Then, the perceptually quantized coefficients
Q
,

from a known viewing distance
d
, are calculated as follows:

(2)

Unlike the classical techniques of visual frequency weighting (VFW) on JPEG2000, which ap-

ply one CSF weight per subband [
1
, Annex J.8], perceptual quantization using CBPF (pSQ)

applies one CSF weight per coefficient over all wavelet planes
ω
s
,
o
. In this section, we only

explain forward perceptual quantization using CBPF (F-pSQ). Thus, Equation
(2)
intro-

duces perceptual criteria of the perceptual
images (1)
to each quantized coefficient of the

used, namely, the range between the minimal and maximal values at
I
ρ
is divided into 128

intervals. Finally, the perceptually quantized coefficients are entropy coded, before forming

the output code stream or bitstream.

(3)
Inverse quantization
(
I
-pSQ): The proposed perceptual quantization is a generalized method,

which can be applied to wavelet-transform-based image compression algorithms such as

EZW, SPIHT, SPECK, or JPEG2000. In this work, we introduce both forward (F-pSQ) and

troducing pSQ is to maintain the embedded features not only of H
i
-SET algorithm but also

of any wavelet-based image coder. Thus, we call CBPF quantization + H
i
-SET = CH
i
-SET or

XSET.

Both JPEG2000 and XSET choose their VFWs according to a final viewing condition. When

JPEG2000 modifies the quantization step size with a certain visual weight, it needs to ex-

plicitly specify the quantizer, which is not very suitable for embedded coding. By contrast,

XSET needs neither to store the visual weights nor to necessarily specify a quantizer in or-

der to keep its embedded coding properties.

The main challenge underlies in to recover not only a good approximation of coefficients
Q

but also the visual weight
α
(
ν
,
r
) (Equation
2
) that weighted them. A recovered approxim-

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