pSQ - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 64

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

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