Image Processing Reference
In-Depth Information
This work is supported by National Polytechnic Institute of Mexico by means of Project No.
20140096, the Academic Secretary and the Commitee of Operation and Promotion of Aca-
demic Activities (COFAA), National Council of Science and Technology of Mexico by means of
Project No. 204151/2013, and LABEX Σ-LIM France, Coimbra Group Scholarship Programme
granted by University of Poitiers and Region of Poitou-Charentes, France.
1 Introduction
Digital image compression has been a research topic for many years and a number of image
compression standards have been created for different applications. The JPEG2000 is intended
to provide rate-distortion and subjective image quality performance superior to existing
standards, as well as to supply functionality [ 1 ] . However, JPEG2000 does not provide the
most relevant characteristics of the human visual system, since for removing information in
order to compress the image mainly information theory criteria are applied. This information
removal introduces artifacts to the image that are visible at high compression rates, because
of many pixels with high perceptual significance have been discarded. Hence, it is necessary
an advanced model that removes information according to perceptual criteria, preserving the
pixels with high perceptual relevance regardless of the numerical information. The Chromatic
Induction Wavelet Model presents some perceptual concepts that can be suitable for it. Both
CBPF and JPEG2000 use wavelet transform. CBPF uses it in order to generate an approxim-
ation to how every pixel is perceived from a certain distance taking into account the value
of its neighboring pixels. By contrast, JPEG2000 applies the perceptual criteria for all coei-
cients in a certain spatial frequency independently of the values of its surrounding ones. In
other words, JPEG2000 performs a global transformation of wavelet coefficients, while CBPF
performs a local one. CBPF atenuates the details that the human visual system is not able to
perceive, enhances those that are perceptually relevant and produces an approximation of the
image that the brain visual cortex perceives. At long distances, the lack of information does
not produce the well-known compression artifacts, rather it is presented as a softened version,
where the details with high perceptual value remain (e.g., some edges).
The block diagram of the X-SET engine for encoding and decoding is shown in Figure 1 . The
source data are an RGB image, which comprises three components, then a color transforma-
tion is first applied over all three components. After the color transformation, each component
is decomposed with a discrete wavelet transform into a set of planes of different spatial fre-
quencies by means of a forward wavelet transformation (9/7 analysis Filter). Then, these coef-
icients are forward perceptually quantized using CBPF, for reducing the precision of data in
order to make them more perceptually compressible. Perceptual quantization is the only re-
sponsible that introduces imperceptible lossless distortion into the image data. Then, H i -SET
algorithm is employed for entropy encoding among quantized coefficients forming the output
bit stream. The decoding process is the inverse of the encoding one. The bit stream is first en-
tropy decoded by means of H i -SET, perceptually dequantized, inverse discrete wavelet trans-
formed and inally inverse color transformed, geting as a result the reconstructed image data.
FIGURE 1 General block diagram for βSET encoding and decoding.
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