Image Processing Reference

In-Depth Information

FIGURE 4
Examples of recovered images of Baboon. (a) JPEG2000 compressed at

0.59 bpp. (b) JPEG2000-F-pSQ compressed at 0.45 bpp.

3.3 Perceptual Inverse Quantization

The proposed perceptual quantization is a generalized method, which can be applied to wave-

let transform-based image compression algorithms such as EZW, SPIHT, SPECK, JPEG2000,

or H
i
-SET.

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

with a certain distortion Λ is decoded from the bitstream by the entropy decoding process.

The VFWs were not encoded during the entropy encoding process, since it would increase the

amount of stored data. A possible solution is to embed these weights
α
(
ν
,
r
) into . Thus, our

goal is to recover the
α
(
ν
,
r
) weights only using the information from the bitstream, namely,

from the forward quantized coefficients .

The reduction of the dynamic range is uniformly made by the perceptual quantizer; thus,

the statistical properties of
I
are maintained in

. Therefore, our hypothesis is that an approx-

imation

of
α
(
ν
,
r
) can be recovered applying CBPF to

, with the same viewing condi-

tions used in
I
. That is,

is the recovered e-CSF. Thus, the perceptual inverse quantizer

or the recovered

introduces perceptual criteria to inverse scalar quantizer and is given

by

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