Image analysis – Image compression or coding – Parallel coding architecture
Reexamination Certificate
1998-03-20
2003-12-30
Do, Anh Hong (Department: 2724)
Image analysis
Image compression or coding
Parallel coding architecture
C382S232000, C382S239000, C382S236000
Reexamination Certificate
active
06671410
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to image processing apparatuses, and more particularly to an image processing apparatus employing fractal block coding.
2. Description of the Related Art
This application is based on Japanese Patent Application No. 9-069935 filed in Japan, the contents of which are hereby incorporated by reference.
The importance of image media has become higher as an image transmission system in accordance with the recent spread of image output devices and printers, as well as computer networks. Since image data have a significant amount of information, processing and transmission of the data in its original state is not efficient taking into consideration the capacity of the memory medium for storing data and the load on the transmission path.
Therefore, some efficient coding technique is generally applied. Research of various coding methods such as JPEG which is now in the standarization progress are carried out for the purpose of achieving a more efficient coding technique.
One noteworthy image compression method is the fractal block coding method by A. E. Jacquin. This method is based on the IFS (Iterated Function System) theory proposed by M. F. Barnsley. According to this method, an image is divided into a plurality of small blocks. Coding is effected by searching for partial self similarity with each block as the basic unit. More specifically, for one block of interest, a block that provides the optimum approximation is sequentially searched for from the entire image. The transformation matrix of the three dimensional reduction affine transformation that is applied at that time is recorded as the compressed data. At the time of reproduction, the recorded three-dimensional reduction affine transformation is iteratively applied on an arbitrary initial image repeatedly to obtain a reproduced image. This method is advantageous in that a high coding efficiency can be expected with respect to data of high self similarity. Furthermore, in contrast to the conventional compression method where data bias is included as redundancy, attention is focused on the fact that the image can be expanded to an arbitrary resolution at the reproduction stage in principle since the image is processed geometrically.
The conventional fractal block coding method will be described hereinafter. In this method, coding is carried out by substitution with the problem of searching for partial self similarity of which there is a density change that is extremely similar in the neighborhood of a portion of interest in an image having a certain density change.
First, the original image that is the subject of compression is divided into several small blocks (range blocks). A region (domain block) having a density change that is similar to that of the range block of interest is searched for in a sequential manner for respective range blocks while applying the reduction transformation shown in FIG.
1
. More specifically, the range block is approximated by an approximation block that has a domain block D subjected to a three-dimensional reduction affine transformation. The three-dimensional reduction affine transformation includes three types of transformations of: area reduction transformation on the plane of the image definition coordinates; pixel allocation transformation (affine transformation); and density transformation (FIG.
2
). The pixel allocation transformation includes the total of eight transformations of rotational transform for every 90 degrees and respective mirror image transformation. The density transformation includes the scale transformation for matching the range of the maximum and minimum values of the density value and the shift transformation for matching the average density value. Mean square error is employed as a barometer for measuring the approximation. This is represented by the following equation (1), where f(x, y) is the density value of range block R and g(x, y) is the density value of an approximation block D′.
MSE
=
1
b
2
∑
(
x
,
y
)
∈
B
(
f
(
x
,
y
)
-
g
(
x
,
y
)
)
2
(
1
)
Determination is made that the optimum approximation is achieved when the mean square error MSE is minimum. Thus, for a range block R of interest, coding is effected with the position of a domain block D achieving the optimum approximation and the transformation matrix of the three-dimensional reduction affine transformation as compressed data.
In other conventional compression methods, the original image is transformed into a coordinate system having a bias statically, and then the bias thereof is included as the redundancy. However, the fractal image compression method is different. Attention is focused on the fractal image compression method as a new method of removing the geometrical structural redundancy on the image plane. Since transformation on the image plane is carried out using compressed data, expansion is allowed at one time in reproduction by preparing the initial image of a desired resolution at the time of reproduction.
However, fractal image compression is disadvantageous in that the computation for searching for an approximation region of each block in the compression stage is extremely time-consuming. Furthermore, block distortion due to the block-by-block basis and distortion in blocks having a sudden density change such as the edge portion where an approximation block that provides sufficient approximation is not found are conveyed to the entire image at the time of reproduction, so that the picture quality of the reproduced image is degraded. It can be said that the picture quality of the reproduced image has not yet achieved an acceptable level for practical usage.
In fractal coding, the picture quality of the reproduced image is degraded at the region exhibiting severe density change such as at the edge portion in an image. The density transformation (scale transformation and shift transformation) carried out when a range block is approximated with a domain block is defined as shown in
FIG. 2
for fractal coding. Since scale transformation is a reduction transformation for matching the maximum and minimum width of the density value of these two blocks, the condition of the following equation (2) must be satisfied between range block R and domain block D.
Density difference &Dgr;R of range block ≦density difference &Dgr;D of domain block (2)
The domain blocks that become the candidates for approximation is limited for the range block having the maximum density difference. The possibility that sufficient approximation is not carried out is high. Thus, degradation easily occurs.
SUMMARY OF THE INVENTION
In view of the foregoing, an object of the present invention is to provide an image processing apparatus that can have picture quality improved in reproducing original image data.
Another object of the present invention is to improve picture quality in reproducing original image data in an image processing apparatus that carries out coding by a fractal block coding method.
A further object of the present invention is to carry out coding according to original image data in an image processing apparatus that carries out coding by a fractal block coding method.
The above objects are achieved by an image processing apparatus set forth in the following.
According to an aspect of the present invention, an image processing apparatus includes a unit for sorting original image data into edge image data and planar image data, a first coder for coding the planar image data sorted by the sort unit according to a fractal block coding method, and a second coder for coding the edge image data sorted by the sort unit according to a coding method different from the fractal block coding method.
The original image has the planar portion image of small density change that is suitable for fractal block coding compressed using the fractal block coding method, and the edge portion image that is not suitable for the fractal block coding method compressed using another codin
Ishikawa Atsushi
Itoh Tetsuya
Nakamura Kazuaki
Tsuboi Tomo
Yamamoto Shinji
Burns Doane , Swecker, Mathis LLP
Do Anh Hong
Minolta Co. , Ltd.
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