Multidimensional multi-valued color image compression and decompression method

A multidimensional multi-valued image compression and decompression method obtains feature points satisfying a specific condition on an equi-luminance line set for each specific luminance value previously determined for an image luminance function, and transmits and records positions and luminance values at the feature points to restore an image. In compression, pixel contours are traced on the equi-luminance line set. Specific equi-luminance line passing point coordinates on the pixel contours are shifted to a central position of each pixel. In decompression, a region where the equi-luminance line on the image is a boundary is divided into a bright and a dark region according to a luminance threshold. The divided regions are filled with bright and dark symbols, respectively, to form a mask representative of the image based on the bright and dark symbols.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates to a multidimensional multi-valued color 
image compression and decompression method, and more specifically to the 
multidimensional multi-valued color image compression and decompression 
method of compressing and decompressing image information effectively in 
such a system that the correspondence between the compression side and the 
decompression side is not guaranteed with respect to pixels or frames on 
time axis, as with the case of the image transmission between two image 
system of different models. 
2. Description the Related Art. 
When image signals are converted into digital image signals on the basis of 
linear quantization (uniform quantization), in general when a difference 
between a representative point and an original point is required to be not 
noticed in a natural image, it is necessary to use the number of bits from 
6 bits (64 gradations) to 8 bits (256 gradations) for each sample value of 
the image signals. Therefore, when the image signals digitalized on the 
basis of uniform quantization are recorded as they are, a great amount of 
image information must be processed. 
To code the image signals on the basis of a lesser amount of image 
information, various methods of compressing the image information, that 
is, the various methods of efficient coding have been so far proposed. 
For instance, there exists such a method of utilizing the human nature as 
to the sense of sight or hearing such that the human sensitivity is high 
when a change is gentle but low when a change is violent, whenever audio 
or video signals change. 
Or else, there exists such a method of utilizing correlation between image 
information values on the time-or space-axis. In this method, using high 
correlation in luminance value between adjacent pixels, a small number of 
approximation values of the original information are transmitted; or 
differences in image signal between pixels or between frames are 
transmitted; or the frequency components are reduced on the basis of the 
fact that the high frequency components are less. 
As described above, after the amount of information for each sample value 
has been reduced, the digital data are recorded, transferred or 
transmitted. Further, after having received and reproduced the digital 
data (whose amount of information has been already compressed) are 
decompressed for restoration of the compressed digital data to the 
original 
The above-mentioned various methods have been so far executed and thereby 
well known. 
In the above-mentioned prior art image information compression methods, 
however, a main stress has been so far laid on how to restore the 
decomposed image signals at the respective pixels under excellent 
conditions. In this case, however, the compression method is usually 
established under the conditions that the number of pixels of the original 
image (on the compression side) matches that of the restored image (on the 
decompressed images). As a result, when the compression and decompression 
operation is effected between images of different numbers of pixels, it 
has been so far necessary to additionally interpolate or reduce the number 
of the pixels after decompression. 
In the prior art image information compression method, this implies that 
only true effective information is not necessarily extracted end then 
restored, but that the image information is dependent, to same extent, 
upon the physical image constituting elements, such as the number of 
pixels, pixel shapes and luminance level. 
On the other hand, as an example in which the number of pixels is extremely 
different from each other between original image and the decompressed 
image, there exists the case where an image photographed by an image 
sensing device is required to be used as a print block copy. In this case, 
the pixel density of an image obtained by sensing device is about 
(500.times.500) per frame at the most. On the other hand, the pixel 
density of an image printed by an electronic photoengraving machine is 
about (several thousands.times.several thousands) per frame, which is 
extraordinarily larger than that of the image sensing device. As a result, 
even if the compression and decompression method is not at all adopted to 
the image information, aliasing occurs due to the enlargement of the 
pixels. 
Further, when interpolation is only effected without enlarging the pixels, 
the weighted mean values of known data must be allocated to a wide 
interpolation area, so that the image inevitably deteriorates due to 
interpolation distortion. 
In contrast with this, in the case where the pixel density of the original 
image is as large as (several thousands.times.several thousands), since 
the correlation between adjacent pixels is extremely high, it is possible 
to compress the image information at a high compression ratio in 
principle. 
In this case, however, in the prior art image information compression and 
decompression method established under the conditions that the number of 
pixels of the original image (on the compression side) matches that of the 
restored image (on decompression side), there arises such a drawback that 
it is impossible to increase the compression ratio. 
To overcome this problem, the applicant of the present invention has 
already proposed a multidimensional multi-valued compression and 
decompression method suitable for when the number of pixels of the 
original image does not match that of the reproduced image, in Japanese 
Patent Application No. 5-39492 (in 1993). 
This method can be summarized as follows: first equi-luminance contour 
lines are obtained on the basis of image information only feature points 
(feature pixels) of the image are extracted on the basis of the 
equi-luminance contour lines to obtain compressed image data; and the 
feature point positions and the luminance values at these feature points 
are both transferred and recorded. In the decompression of the image 
information, the luminance values at pixels other than the feature points 
are decided on the basis of an interpolation plane decided by the feature 
points and a plurality of adjacent feature points. Here, the feature 
points are the negative or positive maximum points of each of the 
curvatures of the equi-luminance contour lines, for instance. Or else, the 
feature points are decided at positions where a difference between a 
straight line (obtained by approximating the equi-luminance contour line) 
and the equi-luminance contour line exceeds a predetermined threshold, 
respectively. 
On the other hand, in order to obtain the equi-luminance contour lines, it 
is necessary to trace the pixels having a specific luminance value. In the 
above-mentioned related image tracing method, the centers of the boundary 
pixels are traced, and a chain code train has been adopted. In this case, 
there exist problems in that an error is produced at a contour between the 
original image and the reproduced image or in that a long processing time 
is required because the amount of information to be process is huge. 
Further, when the luminance values are binarized on the basis of a 
luminance threshold to extract the equi-luminance line, if the 
distribution of extracted equi-luminance line forms a narrow triangular 
shape for instance, there exists such a case that a white region with a 
single pixel width appears. Under these conditions, when the centers of 
the boundary pixels are traced, there exists a case where a center of one 
pixel appears twice. As a result, when a character is enlarged, there 
exists problem in that the white region remains as a straight line with a 
single pixel width. 
To overcome this problem, the applicant of the present invention has 
disclosed a binary image contour tracing method, in Japanese Laid-Open 
Patent Application No. 5-35872 (in 1993). 
In this binary image contour tracing method, the tracing direction of a 
boundary point of a pixel is defined on the basis of white and black at 
four pixels around the boundary point. In more detail, the tracing 
direction is determined in such a direction that black pixels are present 
on the right side and white pixels are present on the left side. In this 
case, when the tracing direction is upward in an image stood vertically, 
the direction decided as [1]; when leftward, the direction is decided as 
[2]; and when downward, the direction is decided as [3]; and when 
rightward, the direction is decided as [0], respectively. 
The above-mentioned directions are allocated to the respective bits of 
4-bit tracing direction flags. In addition, the above tracing direction 
flags are attached to all the boundary points of the image, by setting bit 
[1] to the boundary points to be traced and [0] to the boundary points to 
be not traced. 
Further, the tracing direction flag at a boundary point already traced is 
changed, by retrieving a boundary point on the basis of the tracing 
direction flag and by tracing another boundary point in the tracing 
direction beginning from the retrieved boundary point as a start point. 
The above-mentioned tracing is effected until the all the tracing 
direction flags change to [0000], that is, until the image contour can be 
obtained. 
In the above-mentioned binary image contour tracing method, it is possible 
to trace the boundary pixels (boundary points) by only allocating the 
previously defined tracing directions to an inputted image. In other 
words, since it is unnecessary to retrieve the tracing direction for each 
pixel, it is possible to eliminate a large capacity memory unit and 
further to shorten the processing time thereof. 
In this related method, however, since the image contour is traced along 
the boundary lines of pixels by deciding the tracing direction in such a 
way that the black pixel is present on the right side and the white pixel 
is present on the left side along the tracing direction, when the 
reproduced image is observed, there exists a problem in that a black pixel 
is produced for each pixel on the left side along the tracing direction. 
This causes no problem when the original image is a character, for 
instance. However, when the original image is an ordinary image, this 
causes a deterioration of image quality. 
As a result, when the above-mentioned binary image contour tracing method 
is applied to the already proposed multidimensional image compression and 
decompression method, a method of solving the above-mentioned problems is 
needed. 
In addition, in the already proposed multidimensional image compression and 
decompression method, the equi-luminance contour lines (an equi-luminance 
plane in the case of three dimensions) are approximated by a polygonal 
shape on the basis of the extracted feature points. Further, the luminance 
information at pixels other than the feature points are decided on the 
basis of an interpolation plane (an interpolation solid in the case of 
three dimensions) decided by a plurality of adjacent feature points. 
The above-mentioned equi-luminance contour line is different from the 
original equi-luminance contour line because the information values are 
compressed. Therefore, there exists a problem in that the order of the 
luminance intensities is reversed between the two equi-luminance contour 
lines at some points, with the result that the luminance distribution of 
the reproduced image is not normal and thereby the image quality 
deteriorates. A method of solving this problem has been also required. 
Further, when the reproduced image is of color image, the color image is 
usually divided into three primary color image components, or into a 
luminance component and two chrominance components, before the color image 
is recorded, reproduced, transmitted and processed. Similarly, in the case 
of the compression end decompression of the color images, it is 
conventional to code and decode the above-mentioned respective signal 
components separately. However, when the luminance component and the 
chrominance components are coded and decoded separately in the color image 
compression and decompression, since two different codes are allocated to 
the luminance component and the chrominance components, respectively, it 
is impossible to reduce the amount of codes below a constant value. 
Therefore, it is impossible to solve the above-mentioned problem by simply 
applying the image compression and decompression technique, as proposed by 
the Japanese Patent Application No. 5-39492, to the conventional color 
image compression and decompression processing. On the other hand, when 
the respective feature points are decided by extracting the respective 
equi-luminance lines for each of a plurality of image components for 
constituting the color image, there arises the other problem in that a 
number of feature points increases and further color shearing is produced 
between the image components due to the polygonal approximation executed 
for the information compression. 
SUMMARY OF THE INVENTION 
With these problems in mind, therefore, it is the object of the present 
invention to provide a multidimensional multi-valued image compression and 
decompression method, which can solve the various problems so far not 
solved by the related art. 
To achieve the above-mentioned object, the present invention provides a 
multidimensional multi-valued image compression and decompression method 
of obtaining feature points satisfying a specific condition on an 
equi-luminance line set for each specific luminance value previously 
determined for an image luminance function, and transmitting and recording 
positions and luminance values at the obtained feature points to restore 
an image, which comprises, in compression, the steps of: tracing at least 
one pixel contour on the equi-luminance line; and shifting specific 
equi-luminance line passing-point coordinates on the traced pixel contour 
to a central position of the pixel, respectively. 
Further, in the above-mentioned method, when an initial tracing direction 
of the pixel contour matches a vertical scanning direction of the image, 
the specific equi-luminance line passing-point coordinates are shifted to 
the central position of the pixel locating on the left side of the tracing 
direction, respectively; and when the initial tracing direction of the 
pixel contour matches a horizontal scanning direction of the image, the 
specific equi-luminance line passing-point coordinates are shifted to the 
central position of the pixel locating on the right side of the tracing 
direction, respectively. 
Further, the present invention provides a multidimensional multi-valued 
image compression and decompression method of obtaining feature points 
satisfying a specific condition on an equi-luminance line set for each 
specific luminance value previously determined for an image luminance 
function, and transmitting and recording positions and luminance values at 
the obtained feature points restore an image, which comprises, in 
decompression, the steps of: to obtain at least one equi-luminance line of 
a specific luminance value, dividing a region in which the equi-luminance 
line on the image is a boundary, into a bright region and a dark region 
according to a luminance threshold on the image; and filling the divided 
regions with bright and dark symbols, respectively to form a mask 
representative of the image on the basis of the binary bright and dark 
symbols. 
Further, the above-mentioned method further comprises the steps of: 
arranging a plurality of masks representative of the image in the order of 
intensities of the luminance threshold values corresponding to the 
respective masks; representing the region between two adjacent masks by an 
intermediate luminance value between the luminance threshold values of the 
two adjacent masks; when the relationship between a first luminance value 
of at least one predetermined central pixel in the mask and second 
luminance values of eight pixels around the predetermined central pixel is 
that: (1) the second luminance values are equal to the first luminance 
value, the first luminance value is determined to be undecided; (2) the 
second luminance values are not higher than the first luminance value, the 
first luminance value is determined to be the intermediate value -a, where 
a is a positive value; (3) the second luminance values are not lower than 
the first luminance value, the first luminance value is determined to be 
the intermediate value +a; and (4) the relationship between the first and 
second luminance values are other than the above (1) to (3), the first 
luminance value As determined to be the intermediate value; extracting a 
central line from the region including the pixel of the undecided 
luminance value, to set at least one pixel on the central line of the 
region to the intermediate luminance value. 
Further, the above-mentioned method further comprises the step of: deciding 
a luminance value at a luminance-undecided pixel locating away from the 
central line of the region, in accordance with linear interpolation on the 
basis of two luminance values at two luminance-decided pixels locating on 
both ends of at least one line extending from the luminance-undecided 
pixel in a predetermined direction and on the basks of distances between 
the luminance-undecided pixel and the luminance-decided pixels. 
Further, the above-mentioned method further comprises the step of: deciding 
a luminance value at a luminance-undecided pixel locating away from the 
central line of region, by averaging the luminance values obtained in 
accordance with a plurality of linear interpolations on the basis of 
respective two luminance values at two respective luminance-decided pixels 
locating on both ends of each of a plurality of lines extending from the 
luminance-undecided pixel in a plurality of predetermined directions and 
on the basis of respective distances between the luminance-undecided pixel 
and the luminance-decided pixels along each of a plurality of the 
extension lines. 
Further, the above-mentioned method further comprises the step of: deciding 
a luminance value at a luminance-undecided pixel locating away from the 
central line of the region on the basis of plane interpolation values 
obtained by use of three luminance-decided pixels in the vicinity of the 
luminance-undecided pixel. 
Further, in the above-mentioned method, when a difference in the luminance 
threshold between the two adjacent masks is determined d, a=d/3. 
Further, the present invention provides a color image compression and 
decompression method, which comprises the steps of: obtaining 
two-dimensional image addresses and luminance values at a plurality of 
feature points satisfying a specific condition on an equi-luminance line 
set for each specific luminance value previously determined for luminance 
component for constituting a color image together with chrominance 
components; obtaining the chrominance component values on the basis of the 
chrominance components on the feature points; adding the obtained 
chrominance component values to the two-dimensional address of the 
luminance component at each feature point, as four-dimensional address 
data; setting feature point positions of the chrominance components to 
feature point positions of the luminance component; and transmitting and 
recording the chrominance component values at the positions, to restore an 
image. 
Further, the above-mentioned method further comprises the steps of: setting 
an allowable value for the respective chrominance component values at each 
feature points on equi-luminance line; when the chrominance component 
values at each feature point do not exceed the set allowable value, the 
chrominance component values at a current feature point are determined to 
be the same as chrominance component values at a preceding feature point, 
to reduce the occurrence frequency of the chrominance component values. 
Further, in the above-mentioned method, the chrominance component values 
are obtained in accordance with difference calculus or logarithmic 
difference calculus or vector quantization method.

DETAILED DESCRIPTION OF THE EMBODIMENTS 
The practical Contents of the multidimensional multi-valued color image 
compression and decompression method according to the present invention 
will be described hereinbelow with reference to the attached drawings. 
&lt;Application to Luminance Component&gt; 
First, the principle of the multidimensional multi-valued color image 
compression and decompression method will be explained hereinbelow. 
Where a static monochromatic image is taken into account, if the luminance 
in an image is denoted by z and further the horizontal and vertical 
positions of the image are represented by x and y, respectively, the image 
luminance z can be expressed in general by the following formula (1): 
EQU z=f (x,y) (1) 
FIG. 6 shows an example of the luminance function. Further, if the time 
axis is represented by t, the luminance of a motion picture can be 
expressed in general by the following formula (2): 
EQU z=f (x,y, t) (2) 
Here, if f denotes a multidimensional function, the luminance z in an image 
can be expresses by the following formula (3): 
##EQU1## 
The fact that an image is transmitted is to reproduce the above-mentioned 
luminance function decided on the image transmitting side, on the image 
receiving side. In the general digital image transmissions, the luminance 
function is not handled as being analytical, but handled as a table 
function and the all the table values are transmitted. In the compression 
transmission, the table values themselves are coded at a high efficiency 
by utilization of correlation between adjacent table values. Further, the 
same process as described above is applied to the table values obtained 
after orthogonal transformation. 
In the multidimensional multi-valued color image compression and 
decompression method according to the present invention, the luminance 
function is processed analytically. That is, first equi-luminance lines 
(the contour lines) (the equi-luminance planes in the case of three 
dimensions) are extracted from the image information such as the 
two-dimensional luminance information in the case of a static image or the 
three-dimensional luminance information inclusive of time axis in the case 
of a motion picture. In FIG. 6, an equi-luminance line is shown by L, by 
way of example. After that, feature points of an image are determined at 
such points as the positive or negative maximum points of the curvature of 
the extracted equi-luminance line or as points at each of which a 
difference between the equi-luminance line (or equi-luminance plane) and 
the linear approximation (or plane approximation) of the equi-luminance 
line (or equi-luminance plane) exceeds a predetermined threshold value. 
Further, the positions and the luminance values at the above-mentioned 
feature points are transmitted (or recorded) for image reproduction. At 
this time, the luminance values at the pixels which will not exert a 
serious influence upon the reproduced image are omitted, in order to 
markedly compress the amount of image information. Further, in the image 
decompression, the luminance function is reproduced on the basis of the 
transmitted feature points to reproduce, by interpolation, non-transmitted 
image information. 
Here, the principle of the image information compression and decompression 
(restoration) of the multidimensional multi-valued image compression and 
decompression method according to the present invention will be described. 
An example of multi-valued static image information compression will be 
explained. FIG. 7 shows an equi-luminance image obtained by slicing the 
luminance function as shown in FIG. 6 at a predetermined equi-luminance 
plane. In FIG. 7, a partition enclosed by dot lines indicates each pixel 
plane. Further, a pixel plane shown by oblique lines (referred to as a 
black pixel plane, hereinafter) represents a region (a low luminance 
plane) whose luminance is lower than the equi-luminance plane, and a pixel 
plane shown in white (referred to as a white pixel plane, hereinafter) 
represents a region (a high luminance plane) whose luminance is higher 
than the equi-luminance plane, respectively. 
Now, when boundary lines between the black pixel planes and the white pixel 
planes are traced in accordance with the contour tracing method, as 
disclosed in Japanese Laid-open Patent Application No. 5-35872, an image 
contour as shown in FIG. 8 can be obtained. 
In more detail, the above-mentioned boundary lines are traced by scanning 
the image beginning from the upper left side point of the picture in 
accordance with the raster scanning sequence. Then, as shown in FIG. 7, a 
first boundary and a second boundary can be found. 
When the boundaries are traced beginning from the above-mentioned start 
point under the condition that the high luminance plane is present on the 
left side in the trace direction, there are two cases where the trace 
start direction matches the horizontal scanning direction (CW: clockwise) 
and where the trace start direction matches the vertical scanning 
direction (CCW: counterclockwise). 
In FIG. 8, the trace direction sort flags (CW and CCW) are attached for 
each traced equi-luminance line. After the contour has been obtained by 
tracing the pixel contours for each specific luminance value of the image 
luminance function, as shown in FIG. 8, the coordinates through which the 
specific equi-luminance line is passed (specific equi-luminance line 
passing-point coordinates) are shifted to the central position of each of 
the pixels for constituting the above-mentioned specific luminance 
boundary, as shown in FIG. 9. 
In other words, when the tracing start direction matches the vertical 
scanning direction (in CCW), the equi-luminance line is shifted to the 
central position of each of the pixels located on the left side of the 
tracing direction. Further, when the tracing start direction matches the 
horizontal scanning direction (in CW), the equi-luminance line is shifted 
to the central position of each of the pixels located on the right side of 
the tracing direction. The above-mentioned two shift examples are both 
shown in FIG. 9, respectively. 
As already explained, when an image is reproduced by using the contour 
obtained by tracing pixels as the specific luminance boundary, since one 
black pixel is produced on the specific side along the tracing direction, 
the image quality deteriorates. 
On the other hand, when the coordinates (through which the specific 
equi-luminance line of the pixel contour obtained by the tracing as 
described above is passed) are shifted to the central position of each of 
the pixels for constituting the specific luminance boundary, the 
reproduced image will not deteriorate. 
Next, after the start point has been fixed, the pixels on the 
equi-luminance line are connected in sequence by a virtual line beginning 
from the start point. In this case, the actual pixel (at which a distance 
between the virtual line and the actual pixel existing along the virtual 
line exceeds a predetermined allowable range) is registered as a feature 
point. 
When the feature point is found, the operation similar to the above is 
repeated beginning from the found feature point as a new starting point, 
to register other feature points in sequence. 
The above-mentioned operation is repeated to reach the starting point. 
After that, the obtained feature points are connected to each other to 
obtain a new equi-luminance line as shown in FIG. 10. 
The above-mentioned operation is executed for each equi-luminance plane 
having each different luminance value (threshold). As a result, since the 
luminance function data of the original image can be reduced down to the 
data of each feature point group along each of the equi-luminance lines, 
it is possible to compress the image information. 
Here, the method of decompressing (restoring) the approximate values of the 
luminance function by use of feature point group data will be described 
hereinbelow. 
When the feature points are connected, it is possible to obtain the 
equi-luminance lines on the decompression side, as shown in FIG. 10. 
Here, the important point is that the equi-luminance lines restored on the 
decompression side can be drawn or obtained by vectors designated by the 
end points (feature points). Accordingly, even if the pixel density of the 
image memory on the decompression side is different from the pixel density 
of the image memory on the compression side, the shape of the 
equi-luminance line restored on the decompression side are hardly 
subjected to the physical conditions of the above-mentioned difference in 
pixel density between the compression and decompression sides. 
After that, the pixels through which the equi-luminance lines have passed 
and the region on the right side (low luminance side) of the 
equi-luminance lines are marked by the oblique lines as shown in FIG. 11. 
The same is applied to a predetermined number of equi-luminance lines, 
respectively to form a mask for each equi-luminance line. 
FIG. 12B shows masks for dividing 8-bit digital luminance signals into 
9-step gradation levels by way of example. Here, the luminance gradations 
are 256. Each mask has a predetermined respective threshold. For instance, 
the threshold of the mask No. 4 has a luminance gradation level of 120. 
When this mask is applied to the image luminance, it is possible to divide 
the luminance into a portion having the luminance level more than the 
gradation level 120 and another portion having the luminance level other 
than the gradation level 120. 
FIG. 13 shows examples in which an image having the luminance function as 
shown in FIG. 6 is masked by use of 8 sorts of masks as shown in FIG. 12B. 
The picture represented by mask No. 1 is all colored in white. As shown in 
FIG. 12B, the threshold value of the mask No. 1 is set to a luminance 
value corresponding to the level 30 of the 256 gradations. This indicates 
that when this mask No. 1 is applied, the portion whose luminance value is 
higher than the threshold 30 is colored in white, and the portion whose 
luminance value is lower than the threshold 30 is colored in black. 
Further, in FIG. 13, the fact all the picture represented by the mask No. 
1 is white indicates that there exists no portion whose luminance value is 
lower than the threshold 30 of the 256 gradations in the image. 
The picture represented by mask No. 8 in FIG. 13 is all colored in black. 
As shown in FIG. 12B, the threshold value of the mask No. 8 is set to a 
luminance value corresponding to the level 240 of the 256 gradations. 
Further, in FIG. 13, the fact that all the picture represented by the mask 
No. 8 is black indicates that there exists no portion whose luminance 
value is higher than the threshold 240 of the 256 gradations in the image. 
Further, in the pictures represented by the mask Nos. 2 to 7 shown in FIG. 
13, a part of the picture is black and the other part thereof is white. 
This indicates that two portions whose luminance values are higher or 
lower than the threshold values of the masks coexist. 
Therefore, when the eight masks as shown in FIG. 12B are overlapped one 
another, it is possible to reproduce an image having luminance values of 
nine different gradations. FIG. 14 shows the luminance distribution 
expressed by numerical values, in which the luminance value at the white 
portion of each mask is represented by the numerical value indicative of 
the mask number and further these eight masks are overlapped with each 
other. 
In general, when n masks are used, the respective pixels can be divided as 
follows: 
______________________________________ 
Mask No. 0 .fwdarw. Zero to 1st equi-luminance value pixels 
Mask No. 1 .fwdarw. 1st to 2nd equi-luminance value pixels 
Mask No. 2 .fwdarw. 2nd to 3rd equi-luminance value pixels 
: : : : : : : : 
Mask No. i .fwdarw. i-th to (i + 1)th equi-luminance value pixels 
: : : : : : : : 
Mask No. n .fwdarw. n-th to (n + 1)th equi-luminance value 
______________________________________ 
pixels 
In other words, when n masks are formed, it is possible to classified the 
luminance values at the respective pixels which distribute from 0 to 255 
luminance gradations into (n+1) units of categories. FIG. 12B shows an 
example in which n=8. 
Further, when the luminance values at the respective pixels included in the 
respective categories are represented by an intermediate value 
representative of each of the respective luminance ranges, it is possible 
to reproduce the image by use of 9-level equi-luminance lines, in the case 
of the example as shown in FIG. 12B. 
Here, the luminance interpolation after decompression will be described 
hereinbelow. 
In the afore-mentioned example, the original image is represented by 256 
gradations. On the other hand, the luminance levels are reduced to nine 
levels. Therefore, when the image is decompressed by the interpolation on 
the basis of the reduced luminance gradations of nine luminance levels, it 
is impossible to reproduce the image in a sufficiently high quality. 
Therefore, in order to improve the image quality, before the luminance 
values are interpolated for reproduction, the respective luminance values 
used as values at the interpolation ends are corrected as follows, without 
simply using the intermediate value of each category as the interpolation 
end values: 
Eight adjacent pixels around a central pixel to be decided are observed. 
(1) When the luminaries values at the adjacent pixels are equal to the 
luminance value at the central pixel, [undecided] mark is put to the 
luminance value at the central pixel. 
(2) When the luminance values at the adjacent pixels are not higher than 
the luminance value at the central pixel, a [low] mark is put to the 
luminance value central pixel. 
(3) When the luminance values at the adjacent pixels are not lower than the 
luminance value at the central pixel, a [high] mark is put to the 
luminance value at the central pixel. 
(4) When the relationship in luminance value between the adjacent pixels 
and the central pixel is other than the above (1) to (3), an 
[intermediate] mark is put to the central pixel. 
These examples are shown in FIG. 14. In each of the pixel groups composed 
of 9-pixels and denoted by &lt;&gt;, when the central pixel is noted, &lt;1&gt; 
corresponds to the above rule (1); &lt;2&gt; corresponds to the above rule (2); 
&lt;3&gt; corresponds to the above rule (3); and &lt;4&gt; corresponds to the above 
rule (4), respectively. In accordance with these rules, when the marks are 
denoted as follows: the [undecided] is denoted by [.]; the [low] is 
denoted by [-]; the [high] is denoted by [+]; and the [intermediate] is 
denoted by [=], respectively, the respective pixel groups as shown by &lt;1&gt; 
to &lt;4&gt; in FIG. 14 can be expressed as shown in FIG. 15. Further, FIG. 16 
shows the results obtained by correcting all the pixels as shown in FIG. 
14, in accordance with the rules from (1) to (4) above. 
In FIG. 16, [low (-)] indicates that the luminance value must be adjusted 
being shifted to the lower luminance value; [high (+)] indicates that the 
luminance value must be adjusted being shifted to the higher luminance 
value; [intermediate (=)] indicates that the luminance value is adopted as 
it is; and [undecided (.)] indicates that the luminance value must be 
interpolated. 
FIGS. 18A to 18D, 18E to 18H, 18I to 18L show these corrections. In the 
drawings, &lt;1&gt; to &lt;4&gt; represent the relationship between the central pixel 
and the adjacent pixels (the same as shown in FIG. 15); FIGS. 18A to 18D 
represent the observation results of the adjacent pixels corresponding to 
&lt;1&gt; to &lt;4&gt;, respectively; FIGS. 18E to 18H represent the luminance values 
(before adjustment) of the respective pixels by use of bar chart heights; 
and FIGS. 18I to 18L represent the luminance values (after adjustment) of 
the respective pixels by use of bar chart heights. 
In the case of FIG. 18A, since all the luminance values at the adjacent 
pixels are equal to each other, the luminance value at the central pixel 
is undecided (corresponding to &lt;1&gt;,). In the case of FIG. 18B, since the 
luminance values at the adjacent pixels are not higher than that at the 
central pixel, the luminance value at the central pixel is shifted to 
[low] side (corresponding to &lt;2&gt;,). In the case of FIG. 18C, since the 
luminance values at the adjacent pixels are not lower than that at the 
central pixel, the luminance value at the central pixel is shifted to 
[high] side (corresponding to &lt;3&gt;). In the case of FIG. 18D other than the 
above cases, the luminance value at the central pixel is set to 
[intermediate] (corresponding to &lt;4&gt;). 
After that, the central lines (a point group of equi-distances from the 
ends of the respective regions) in the [undecided] regions are extracted, 
and [intermediate] marks are put on the pixels on these lines. These steps 
are shown in FIGS. 19, 20, 21 and 22. That is, FIG. 19 shows the luminance 
values before adjustment at only the [undecided] regions; FIG. 20 shows 
the regions to be decided by putting a mark [*] thereon by abstracting the 
practical luminance values. FIG. 21 shows the [intermediate] marks ([=]) 
and the luminance values at the pixels on the central lines in the 
corresponding regions. FIG. 22 shows all the marks expressed in accordance 
with the rules from (1) to (4). 
Here, the rule is decided that the mask thresholds shown in FIG. 12B are 
allocated to the above-mentioned marks as follows (See, FIG. 12A.): 
1) The threshold of the intermediate-level luminance value of the 
corresponding luminance category is allocated to [intermediate]. 
2) The threshold of the low-level luminance value of the corresponding 
luminance category is allocated to [low]. Here, low-level luminance 
value=intermediate-level luminance value--(category luminance range/3). 
3) The threshold of the high-level luminance value of the corresponding 
luminance category is allocated to [high]. Here, high-level luminance 
value=intermediate-level luminance value + (category luminance range/3). 
In FIG. 23, the [undecided] marks [.] still remain after the 
above-mentioned correction. These luminance values at the marked pixels 
can be obtained on the basis of the interpolation principle as shown in 
FIG. 25. In the interpolation method, the unknown luminance value can be 
obtained on the basis of the known luminance values at the pixels obtained 
by retrieving in both the vertical and horizontal scanning directions 
beginning from the unknown pixel (e.g., px in FIG. 25) and on the basis of 
the predetermined distances. In more detail, the luminance value at the 
luminance-undecided pixel (px) is decided in accordance with the linear 
interpolation method on basis of the luminance values (z1 and z2, or z3 
and z4) end the distances (r1 and r2, or r3 and r4) at the luminance-known 
pixels (p1 and p2, or p3 and p4) locating on both ends of the line 
extending in a predetermined direction from the luminance-undecided pixel 
px. Further, the obtained linear interpolation values are averaged as the 
interpolated luminance value at the luminance-undecided pixel. 
In actual interpolation, the luminance value at luminance-unknown pixel can 
be obtained as shown in FIG. 26. First, the pixels of the known luminance 
values are retrieved in the vertical and the horizontal directions and the 
intermediate direction between both the vertical and horizontal directions 
beginning from the pixel px. By this retrieval, the distances from the 
unknown pixel px and the known pixels and the luminance values at the 
known pixels can be obtained. FIG. 26 shows the respective luminance 
values (z1 to z8) at the respective pixels (p1 to p8 and the respective 
distance values (r1 to r8) between the respective pixels (p1 to p8) and 
the unknown pixel (px). By using these values, the luminance value zx at 
the unknown pixel px can be obtained as. 
EQU zx=.SIGMA.(zi/ri)/.SIGMA.(1/ri) (4) 
FIGS. 17A to 17F show the principle of the interpolation in a more 
understandeble one-dimensional way, in which the one-dimensional luminance 
distribution curve alter decompression (e.g., the luminance distribution 
curve along the pixels arranged on single horizontal scanning line) is 
shown. In FIGS. 17A to 17C, the abscissa indicates the pixel positions, 
and the ordinate indicates the luminance values. Further, the bar chart 
indicates the luminance value obtained by the afore-mentioned masks. 
Further, the black points indicate the luminance values at the mask 
boundary pixels, and the line connecting the black points indicates the 
luminance distribution curve obtained after interpolation. Further, FIGS. 
17D, 17E and 17F represent the luminance distribution curves corresponding 
to FIGS. 17A, 17B and 17C, respectively. In FIG. 17A, only the higher 
values of the mask are shown, and the remaining luminance values are 
interpolated, which corresponds to the method of determining only the 
luminance values at the pixels along the equi-luminance line. In this 
method, stepped portions are produced in the luminance distribution curve 
as shown in FIG. 17D. 
In contrast with this, FIG. 17B shows the method where the luminance values 
are adjusted on the basis of [low], [intermediate] and [high]. In this 
method, the smooth luminance distribution curve can be obtained, as shown 
FIG. 17E. 
Further, FIG. 17C shows the method where the [intermediate] value is set to 
the pixel at the middle line. In this method, the luminance values can be 
restored under consideration of slopes toward the top or bottom of the 
luminance value, so that the more smoother luminance distribution curve 
can be obtained, as shown in FIG. 17F. 
When three adjacent pixels are selected from the two adjacent 
equi-luminance lines in a plurality of equi-luminance lines and further 
when the luminance value between the two equi-luminance lines is 
interpolated on an interpolation plane formed on the basis of the three 
pixels, it is possible to obtain the one-dimensional luminance 
distribution curve, as shown in FIG. 17A. FIG. 24 shows the linear 
interpolation results obtained as described above. Further, FIG. 17C shows 
the same results in one-dimensional way. 
A practical construction for realizing the multi-dimensional multi-valued 
color image compression and decompression method according to the present 
invention will be described hereinbelow with reference to the attached 
drawings. Further, although only the two-dimensional compression and 
decompression method will be described in detail in the following 
description, the method according to the present invention can be of 
course applied to the three-dimensional compression and decompression 
method. 
In the compression side construction of the multi-dimensional multi-valued 
color image compression and decompression method according to the present 
invention shown in FIG. 1, an image source (e.g., TV camera) 1 generates 
video signals in accordance with the predetermined standard TV system. As 
this image source 1, any types of video signal generating apparatus can be 
used, as far as the image information to be compressed and decompressed by 
the multidimensional multi-valued color image compression and 
decompression method according to the present invention can be generated. 
The image source 1 shown in FIG. 1 is of the type which can generate three 
primary color signals. The three primary color signals generated by the 
image source 1 are applied to an ADC (analog-digital conversion section) 
2. 
The ADC 2 decomposes the luminance signal for each image into a 
predetermined number of pixels (e.g., 512 pixels in the image horizontal 
direction and 480 pixels in the image vertical direction) along the image 
horizontal and vertical directions, and generates digital luminance 
signals Y of a predetermined number (e.g., 8-bit) of bits. The generated 
digital luminance signals Y are applied to each of signal processing 
sections 4-1, 4-2, . . . 4-n, respectively. Further, digital chrominance 
signals corresponding to the pixels are formed as the chrominance signals, 
and the formed digital chrominance signals are supplied to a color memory 
3. 
Each practical construction of the signal processing sections 4-1, 4-2, . . 
. 4-n is shown in the signal processing section 4-1 enclosed by a 
dot-dashed line frame. In more detail, a specific luminance level 
information extract section 5 binarizes the image information to be 
compressed on the basks of a predetermined luminance threshold different 
from each other according to each of the signal processing sections 4-1, 
4-2, . . . , 4-n, and outputs the binarized image information together 
with the image addresses. 
FIG. 2 shows a structural example of the specific luminance level 
information extract section 5. In FIG. 2, the digital luminance signals Y 
are applied from the ADC 2 through an input terminal 5a. In addition, a 
clock signal is applied to the same section 5 from a controller (not 
shown) through another input terminal 5b. 
In more detail, the digital luminance signals Y applied to the specific 
luminance level information extract section 5 is compared with a specific 
luminance threshold applied by a luminance threshold setting section 52 by 
a comparator (magnitude comparator) 51, and then outputted through an 
output terminal 5c as the binary outputs of the luminance signals Y. To 
the above-mentioned luminance threshold Betting section 52, a 
predetermined binary threshold value is set from a ROM, a DIP switch, a 
fuse array, etc. These luminance thresholds are previously determined for 
operating the multidimensional multi-valued color image compression and 
decompression system, for instance such as the first to eighth thresholds 
as shown in FIG. 12B by way of example. 
The binary outputs of the digital luminance signals outputted by the 
specific luminance level information extract section 5 are stored in a 
luminance memory 6. The binary outputs of the digital luminance signals 
stored in the luminance memory 6 are read on the basks of addresses 
designated by the luminance memory address output signals outputted by a 
address counter 72 (shown in FIG. 3) of a contour line tracer 7, and then 
supplied to a decide section 71 (as shown in FIG. 3) of the contour line 
tracer 7. 
Now, the binary digital luminance signals outputted by the specific 
luminance level information extract section 5 are the binary signals 
representative of specific luminance thresholds, for instance such as to 
obtain the luminance state as shown in FIG. 7. The binary signals read 
from the luminance memory 6 are applied to the decide section 71 (as shown 
in FIG. 3). The decide section 71 starts scanning the pixel group (such as 
a pixel group binarized as shown in FIG. 7), in accordance with the raster 
scanning sequence beginning from the upper left corner of the picture. 
Further, when a first boundary is found, the decide section 71 starts to 
trace the contour of the pixels having the specific luminance value 
beginning from the first boundary. Here, when the trace direction is 
determined in such e way that the higher luminance plane is always present 
on the left side thereof, there are two cases where the trace start 
direction matches the horizontal scanning direction (CW: clockwise) and 
where the trace start direction matches the vertical scanning direction 
(CCW: counterclockwise). The decide section 71 traces the pixel contour as 
described above, and transmits the output signal (address strobe) 
indicative of the contour line information through the output terminal 7c 
(shown in FIG. 3). 
Information H and V indicative of the stepping hysteresis (the trace 
direction changes to the horizontal direction and the vertical direction) 
obtained when tracing the contour of the pixels having the above-mentioned 
specific luminance value are applied to an address counter 72 via a 
multiplexer 73. Therefore, the address counter 72 outputs a sequential 
address train on the basis of the stepping hysteresis in sequence as the 
luminance memory address output through the output terminal 7b (shown in 
FIG. 3), and further as the contour line address output through the output 
terminal 7d (shown in FIG. 3). 
The contour line information output signals (address strobe through the 
terminal 7c in FIG. 3) and the contour line address output (through the 
terminal 7d in FIG. 3) are both applied to the contour line address last 8 
(shown in FIG. 1). After having stored the above-mentioned contour line 
address outputs, the contour line address list 8 shifts the coordinates 
through which the specific equi-luminance line is passed to each of the 
central positions at the pixels for constituting the above-mentioned 
specific luminance boundary, as shown in FIG. 9. 
The address outputs of the central positions at the respective pixels for 
constituting the specific luminance boundary outputted by the contour 
address list 8 are supplied to a polygonal approximation address list 9, 
respectively. FIG. 4 shows a practical example of this polygonal 
approximation address list 9. In FIG. 4, the address information 
indicative of the central positions at the respective pixels for 
constituting the specific luminance boundary outputted by the contour line 
address list 8 are inputted through an input terminal 9a of the polygonal 
approximation address list 9. These inputted address information through 
the input terminal 9a is supplied to an end point address register 91 and 
a shift register 93. 
The output of the end- point address register 91 is applied to an 
interpolation address calculation section 92. To this interpolation 
address calculation section 92, address information indicative of the 
first memory section of the shift register 93 is also given. 
The interpolation address calculation section 92 replaces a distance 
between each feature point and the interpolation straight line, with a 
simplified value for comparison. Here, now, the image horizontal scanning 
direction is determined as Yv axis and the image vertical scanning 
direction is determined as Yv. Here, when the intersection angle between 
the interpolation line and the horizontal scanning direction Xh axis is 
less than 45 degrees, the distance between the interpolation line and the 
feature point on the vertical scanning direction Yv axis is set to the 
simplified value. On the other hand, when the intersection angle between 
the interpolation line and the vertical scanning direction Yv axis is an 
angle except 45 degrees, the distance between the interpolation line and 
the feature point on the horizontal scanning direction Xh axis is set to 
the simplified value. 
The threshold of the intersection angle is determined to be 45 degrees on 
condition that one pixel is square. In other words, when the absolute 
value of the intersection angle is less than 45 degrees (or other than the 
above), the contour line address information is inputted to the first 
accumulation section of the shift register 93 in sequence, and further 
held thereat in sequence. The interpolation address group at the positions 
which correspond to the horizontal scanning direction (Xh axis) addresses 
(or to the vertical scanning direction (Yv axis) addresses) (e.g., the 
address values calculated on the basis of the endpoint addresses and the 
sequential address information stored in the first accumulation section of 
the shift register 93) are supplied to the register 94 as the 
interpolation addresses. 
Further, the contour line pixel addresses stored in the shift register 93 
and the interpolation addresses stored in the register 94 are applied to 
comparison extractors C2, C3, . . . Cn, respectively, as shown in FIG. 4. 
When a difference in absolute value between the contour line address and 
the register interpolation address exceeds a predetermined value, each of 
the comparison extractors C2, C3, . . . Cn outputs the comparison result 
to the feature point address register 95, so that the address at the 
feature point is stored in the feature point address register 95. At the 
same time, the address value at the feature point is given to the end 
point address register 91 as the new end point address value. 
The address information indicative of the central positions at the 
respective pixels for constituting the specific luminance boundary 
(supplied to the input terminal 9a of the polygonal approximation address 
list 9) is the tracing start point address information. Upon start of the 
operation of the polygonal approximation address list 9, the tracing start 
point address information is stored in the end point address register 91, 
and in the first accumulation section 1 of the shift register 93 at the 
same time. Since the sequential contour line address group outputted by 
the con,our line address list 8 is kept supplied to the polygonal 
approximation address list 9, the stored contents are shifted in sequence 
in the respective accumulation sections 1, 2, 3, . . . , n of the shift 
register 93. 
At this time, the stored contents of the end point address register 91 are 
not changed. Only the contents stored in the first accumulation section 1 
of the shift register 93 are outputted as an updated linear interpolation 
value address group. 
As described above, whenever a linear interpolation value address group is 
outputted, under consideration of the slope of the straight line, the 
interpolation value address group whose horizontal (or vertical) direction 
addresses correspond to the respective accumulation sections 1, 2, . . . , 
n of the shift register 93 are outputted to the accumulation sections 2, 
3, . . . , n of the register 94. 
When a difference in absolute value (which corresponds to a distance 
between the contour line and the interpolation straight line) between the 
input information supplied by each of the accumulation sections 2, 3, . . 
. ,n and the input information supplied by each of the accumulation 
sections 2, 3, . . . , n of the register 94 exceeds a predetermined value, 
each of the comparison extractors C1, C2, C3, . . . , Cn recognizes the 
contour line address point as a feature point, respectively. The 
recognized feature points are stored in the feature point address register 
95. 
When a plurality of the comparison extractors C2, C3, . . . , Cn output the 
feature point information at the same time, the con, our line address 
nearer to the address value stored in the end point address register 91 is 
adopted as a new end point address. The adopted address value is stored in 
the feature point address register 95, and further the new end point 
address is stored in the end point address register 91. In the above case, 
even if a plurality of feature points are neglected, it is unnecessary to 
restore the feature point information as the feature point addresses. 
The feature point address group outputted separately from the respective 
signal processing sections 4-1, 4-2, . . . 4-n shown in FIG. 1 for each 
different luminance threshold are given to a coding and transmitting 
section 11 via the multiplexer 10. To this coding and transmitting section 
11, the chrominance signal components are also supplied from the color 
memory 3 via the multiplexer 10. Therefore, the coding and transmitting 
section 11 codes these supplied signals in accordance with a known 
efficient coding (e.g., Huffman code), and transmits (or record) the coded 
signals to a receive side (or reproduce side) via a transmission line (or 
recording medium) 12. 
Further, in the embodiment as described above, feature points are decided 
as follows: 
Starting from the already-detacted feature point pixel, the pixels are 
traced in a certain direction along a contour line (a Contour plane in the 
case of the three-dimensional luminance information). In this case, when a 
distance between a virtual straight line connected between the arrival 
pixel and the feature point and another virtual straight line connected 
between the previous arrival pixel and the feature point exceeds a 
predetermined threshold, the arrival pixel is decided as the feature 
point. 
In the embodiment of the present invention, however, the feature point 
address group can be also decided for each different luminance threshold 
as follows: (1) the pixels at which the positive or negative,curvature of 
the equi-luminance line (contour line) (the equi-luminance plane in the 
case of the three-dimensional luminance information) of the luminance 
function becomes the maximum are decided as the feature points or (2) the 
pixels at which the curvature of the equi-luminance line (contour line) 
(the equi-luminance plane in the case of the tree-dimensional luminance 
information) exceed a predetermined threshold angle are decided as the 
feature points. 
The decompression side of the multidimensional multi-valued color image 
compression and decompression method according to the present invention 
will be described hereinbelow with reference to the attached drawings. 
That is, the case where the luminance function of an original image is 
decompressed or restored will be explained. 
In FIG. 5, a receive decoder 13 decodes the high-efficiency coded signals 
supplied through the transmission line 12 (or the recording medium), and 
then gives these decoded signals to signal processing circuits 14-1, 14-2, 
. . . 14-n, respectively. A plurality of these signal processing circuits 
14-1, 14-2, . . . 14-n are provided so as to one-to-one correspond to a 
plurality of the signal processing circuits 4-1, 4-2, . . . 4-n provided 
on the compression side of the multidimensional multi-valued color image 
compression and decompression method according to the present invention. 
The above-mentioned receive decoder 13 decodes the high-efficiency signals 
compressed by the signal processing sections 4-1, 4-2, . . . , 4-n, and 
distributes these compressed and decoded signals to the signal processing 
sections 14-1. 14-2, . . . , 14-n, respectively. 
The feature point addresses supplied from the receive decoder 13 to the 
signal processing circuits 14-1 in sequence are stored in an end point 
address register 15 and another end point address register 16. Further, 
the stored feature point addresses are given from the end point address 
register 15 and the end point address register 16 to a polygonal 
interpolation mask forming section 18, as the both end point addresses. 
The polygonal interpolation mask forming section 18 calculates the 
addresses at the two end point pixels for linear interpolation, and stores 
the calculated addresses in a multi-pore local image memory 17 at a 
designated luminance value. 
Further, when the new feature address is supplied from the receive decoder 
13 to the signal processing circuit 14-1, the supplied new feature point 
address is stored in the end point address register 15 as the new endpoint 
address. At the same time, the end point address stored in the end point 
address register 15 is shifted to the end point address register 16. 
Therefore, the polygonal interpolation mask forming section 18 calculates 
the addresses at the two end point pixels for linear interpolation. The 
calculated addresses are stored in the multi-port local image memory 17 as 
a designated luminance value. 
Whenever the new feature point address is supplied from the receive decoder 
13 to the signal processing circuit 14-1, the polygonal interpolation mask 
forming section 18 repeats the above-mentioned operation to calculate new 
interpolation lines in sequence, and stores the calculated addresses in 
the local image memory 17 as a designated luminance. 
Further, when a closed curve is formed by the above-mentioned interpolation 
lines, the polygonal interpolation mask forming section 18 forms a mask of 
a specific luminance level by allocating the inside of the closed curve to 
a designated luminance in the local image memory 17. 
As described above, it is possible to form a mask composed of binary (light 
(or bright) and shade (or dark)) symbols for each of the specific 
luminance threshold values in such a way that a bright region defined by 
use of the equi-luminance line as a boundary is marked by the light symbol 
and a dark region defined by use of the equi-luminance line as a boundary 
is marked by the dark symbol. 
As described above, each mask is formed for each corresponding luminance 
threshold value in each of the signal processing circuits 14-1, 14-2, . . 
. , 14-n. 
After the above-mentioned operation has been completed by the polygonal 
interpolation mask forming section 18, a multi-value decide operator 19 
and a skeleton luminance decide section 20 operate in such a way that the 
luminance plane of the local image memory 17 can be changed into the 
multi-valued luminance plane. In more detail, as already explained, in 
order to improve the image quality to be reproduced, when the luminance 
values are interpolated for reproduction, the luminance value at each 
interpolation end point is corrected as follows, without simply setting 
the luminance values at both end points to an intermediate value of the 
category: 
First, the masks are arranged in the order of intensities of the luminance 
threshold values, and the region between the adjacent masks is marked by 
an intermediate luminance value between the luminance threshold values of 
the two adjacent masks. 
Next, eight adjacent pixels around a central pixel to be studied are 
observed 
(1) When the luminance values at the adjacent pixels are equal to the 
luminance value at the central pixel, a [undecided] mark is put to the 
luminance value at the central pixel. 
(2) When the luminance values at the adjacent pixels are not higher than 
the luminance value at the central pixel, a [low] mark is put to the 
luminance value at the central pixel. 
(3) When the luminance values at the adjacent pixels are not lower than the 
luminance value at the central pixel, a [high] mark is put to the 
luminance value at the central pixel. 
(4) When the relationship in luminance value between the adjacent pixels 
and the central pixel is other than the above (1)to (3), an [intermediate] 
mark is put to the central pixel. 
Further, the skeleton luminance decide section 20 operates in such a way 
that the central lines in the [undecided] regions (skeleton: a point group 
equidistant away from the region end) can be extracted and the luminance 
value at the pixel on the central line in the above-mentioned region is 
set to an intermediate value of the luminance threshold values of both the 
masks (as shown in FIGS. 13 to 16). The decided luminance values are 
stored in the local image memory 17. 
As described above, the luminance decide operation can be completed by the 
multi-value decide operator 19 and the skeleton luminance decide section 
20. After that, the luminance interpolation plane calculate section 21 
decides the luminance values at the luminance undecided pixels as 
[intermediate level], [low level], and [high level], as shown in FIG. 12A. 
Or else, after the luminance value at the pixel on the central line of the 
region is set to an intermediate value between the luminance threshold 
values of both the masks, the luminance values at the remaining luminance 
undecided pixels are decided by the linear interpolation method, as shown 
in FIG. 25. In more detail, the luminance value at the luminance-undecided 
pixel (px) is decided in accordance with the linear interpolation method 
on the basis of the luminance values (z1 and z2, or z3 and z4) and the 
distances (r1 and r2, or r3 and r4) at the luminance-known pixels (p1 and 
p2, or p3 and p4) locating on both ends of the lane extending in a 
predetermined direction from the luminance-undecided pixel px. In 
practice, as shown in FIG. 26, 8 pixels decompressing in four directions 
are taken into account. That is, the luminance value at the 
luminance-undecided pixel is extending in accordance with the linear 
interpolation method by use of the luminance values and the distances the 
luminance-known pixels located on both the ends of each of straight lines 
extending in predetermined different directions from the luminance 
undecided pixel. Or else, the luminance value at the luminance-undecided 
pixel is decided by use of a plane-interpolated value obtained by the 
luminance values at three luminance known pixels adjacent to the luminance 
undecided pixel. 
The image signals decompressed through the signal processing by the 
respective signal processing circuits 14-1, 14-2, . . . , 14-n are stored 
in the image memories 22 and 23. These two image memories 22 and 23 are so 
operated as to repeat the write operation and the read operation 
alternately in sequence with respect to each other. The image signals read 
from the image memories 22 and 23 are supplied to a video signal generator 
24. The video signal generator 24 generates video signals conforming to a 
TV system of a specific scanning standard, and supplies the generated 
video signals to a monitor TV, so that a reproduced image can be displayed 
on a display picture of the monitor TV 25. 
&lt;Application to Chrominance Components&gt; 
First, the principle of processing the chrominance components in the 
multidimensional multi-valued color image compression and decompression 
method according to the present invention will be described hereinbelow. 
The color image information can be divided into the luminance component and 
the chrominance components of sorts, as shown in FIG. 31. That is, the 
chrominance components can be divided into the chrominance U (B-Y) and the 
chrominance V (R-Y). 
In the case where the image information is expressed by the RGB components, 
the image information is converted into the luminance component and the 
chrominance components of two sorts. 
With respect to the separated luminance component, the equi-luminance lines 
are extracted for each of a plurality of luminance thresholds, in 
accordance with the method as already explained in detail under 
&lt;Application to luminance component&gt;. On the basis of the equi-luminance 
lines, the feature points are extracted by the polygonal approximation and 
in accordance with the predetermined rules. The luminance component 
information of an image is compressed down to the coordinates and the 
luminance values at these feature points. 
FIGS. 32A and 32B show an example, in which an equi-luminance line obtained 
by a certain threshold is drawn by the feature points. In the drawing, the 
feature point information is a series of coordinates as (x1, y1), y2), . . 
. , (x5, y5). 
Here, as show in FIGS. 33A and 33B, the chrominance component values at 
pixels whose image addresses are represented by a series of the 
above-mentioned coordinates are retrieved. In this case, in correspondence 
to a series of The coordinates of the feature points, is is possible to 
obtain (u1, u2, . . . , u5) as the chorominance components U (B-Y) and 
further (v1, v2 . . . , v5) as the chrominance components V (R-Y). By 
adding these values coordinate values (x1, y1), (x2, y2), . . . , (x5, y5) 
of feature points, a series of new four-dimensional data can be obtained 
as (x1, y1, u1, v1), (x2, y2, u2, v2), . . . , (x5, y5, u5, v5). 
In other words, the color image information can be compressed end coded to 
a series of the four-dimensional data. Further, in the actual image, since 
the sampled chrominance components are often used, when the chrominance 
components corresponding to the coordinate values obtained on the basis of 
the equi-luminance line are required, it is necessary to retrieve the 
chrominance components on the basis of the addresses obtained by reducing 
the x-coordinate value and y-coordinate value by half. 
Or else, it is necessary to obtain the chrominance component values by 
developing the chrominance components into a plurality of pixels (whose 
number is equivalent that of the luminance component values) in accordance 
with the interpolation calculations as shown in FIGS. 34A and 34B. FIGS. 
34A and 34B show a method of obtaining some unknown chrominance components 
on the basis of the obtained chrominance components (U in this case). 
As described above, the principle of allocating the chrominance components 
to the feature points of the equi-luminance lines has been explained. In 
practice, however, the following correction is added: 
At the boundaries at which color changes, when only single equi-luminance 
line is extracted, the chrominance value is decided to any one of both the 
colors or an intermediate color between both the colors. This implies that 
the color of the original image cannot be reproduced. In particular, when 
the number of the feature points of equi-luminance line is small, the 
image quality deteriorates markedly. To overcome this problem, chrominance 
component values on both sides of the equi-luminance ling are added as the 
chrominance component values on the equi-luminance line. 
When a series of the coordinate points as (x1, y1, (x2, y2), . . . , (x5, 
y5) are obtained as the feature point information as shown in FIGS. 32A 
and 32B, there are retrieved the chrominance component values u11, u12 . . 
. . , u15 and v11, v12, . . . , v15 at the pixels located on the right 
sides of these corresponding coordinate values and the chrominance 
component values ur1, ur2 . . . , ur5 end vr1, vr2 . . . , vr5 at the 
pixels located on the left sides of these corresponding coordinate values 
from chrominance components. 
Here, the positions of both right and left side pixels can be obtained as 
follows: since the equi-luminance line is obtained by connecting a series 
of coordinate points, both the right and left sides of each feature point 
along the advance direction are obtained by deciding each directional 
vector at each feature point. 
FIG. 39 shows the partial coordinate points (x2, y2 (x3, y3) and (x4, y4) 
of the equi-luminance line shown in FIGS. 32A and 32B. Here, when noting 
the coordinate point (x3, y3), a bisector extends from the coordinate 
point (x3, y3) between the two other coordinate points (x2, y2) and (x4, 
y4). On the basis of this bisector, the right end left pixels can Be 
decided, and eight adjacent pixel positions are determined around the 
coordinate point (x3, y3) as shown in FIG. 40, in which the right and left 
side pixels of the coordinate point (x3, y3) are shown. Further, FIG. 41 
shows the coordinate values of the eight adjacent pixels around the 
central pixel. 
As described above, the right and left pixels are decided around each of 
the feature points. On the basis of these chrominance components (as shown 
in FIGS. 42A and 42B) at these pixels, it is possible to form a new 
sequence of six-dimensional data as (x1, y1, u11, v11, ur1, vr1), (x2, y2, 
u12, v12, ur2, vr2), . . . , (x5, y5, u15, v15, ur5, vr5), with the result 
that the color image can be compress and coded. 
The method of using representative values for the chrominance components at 
some pixels will be described hereinbelow. This method is adopted to 
reduce the amount of information of the chrominance components added at 
the respective feature points, by neglecting the upper and lower small 
values to such an extent as not to exert harmful influence upon 
the-quality of the restored image. 
First, an allowable value for the Chrominance component values is 
determined. The appropriate error allowable value is obtained empirically 
on the basis of the amount of transmitted information and the required 
quality of the restored image. Here, the chrominance components (u1, v1) 
at the start point (x1, y1), of each loop on the equi-luminance line are 
determined as the initial values. Further, when the difference between the 
chrominance components (u2, v2) at the succeeding feature point (x2, y2) 
and the chrominance components at the preceding point does not exceed the 
allowable value, the chrominance component values at the preceding point 
are used as they are. 
The above-mentioned processing is executed in sequence along a series of 
the feature points. However, when the difference in the chrominance 
components exceeds the allowable range, the chrominance component values 
at the current feature point are adopted as the new representative value, 
until the processing reaches the end point of the loop. FIGS., 38A and 38B 
show a practical example, in which the chrominance components can be 
reduced, respectively when the allowable value is set to five. In FIGS. 
38A and 38B, the chrominance values u change at the feature points 1, 6, 
7, 10 and 12, and the chrominance values v change at the feature points 1, 
6, 7 and 10. Only when the chrominance components change during 
transmission of the image information, since the chrominance components 
are transmitted, it is possible to reduce the amount of information to be 
transmitted markedly. 
The method of restoring the chrominance-component image on the basis of the 
feature point data coded as described above will be described hereinbelow. 
By connecting a series of the coordinate points transmitted as the feature 
points on the equi-luminance line, it is possible to draw the lines on the 
decompression side, as shown in FIGS. 35A and 35B. Here, in Order to 
restore the chrominance Component image, it is necessary to obtain the 
chrominance component image on the basis of the chrominance component 
values belonging to the feature points (pixels) on the lines. By the 
method using the representative values as described above, since the 
chrominance component values can be decided at all the feature points, the 
chrominance values at the end points of the drawn line are determined by 
use of the values at the respective feature points. On the other hand, the 
value at an intermediate pixel between the two end points can be decided 
in accordance with the linear interpolation between both the end points, 
as shown in FIGS. 36A and 36B, in which u' (unknown) is decided by u1 and 
u2 (both known). After the lines have been drawn on the basis of all the 
feature points of the equi-luminance line and further the chrominance 
component values have been decided, the chrominance component values at 
the unknown pixels are obtained in accordance with the linear 
interpolation by retrieving the pixels having the known chrominance 
components in the horizontal, vertical and oblique directions and by using 
the obtained values at the adjacent pixels and the distances between them, 
as shown in FIG. 37. The above-mentioned processing is executed in both 
the chrominance components U and V all over the image picture. The 
obtained chrominance components restored in accordance with the method as 
described above are synthesized with the restored luminance component to 
obtain a reproduced color image. 
Further, the method of restoring the chrominance components on the basis of 
the feature point data including the chrominance components at the right 
and left pixels will be described hereinbelow. 
The equi-luminance line can be drawn on the decompression side by 
connecting a series of the coordinate points transmitted as the feature 
points of the equi-luminance line. Here, since the chrominance component 
values at both the right and left pixels are added, when the line is 
drawn, the pixel positions shifted to the right and left sides are 
obtained to draw lines on both the right and left sides thereof. Further, 
after the corresponding chrominance component values are set to the end 
points, respectively, the other chrominance component values on the line 
can be decided in accordance with the linear interpolation. Further, the 
chrominance component value at the unknown pixel can be obtained by the 
interpolation in the same way as with the case of using a single 
chrominance component value. 
The above-mentioned processing is executed for both the chrominance 
components U and V. Both the obtained chrominance components are 
synthesized with the luminance component to restore a color image. 
A practical structural example for realizing the multidimensional 
multi-valued color image compression and decompression method according to 
the present invention will be described hereinbelow in detail. 
FIGS. 27 to 29 are block diagrams showing the compression side, and FIG. 30 
is a diagram showing the decompression side. After an image signal 
inputted by the image source 1 has been A/D converted by the ADO 
(analog-digital convertor) 2 and further separated into the luminance 
component Y and the chrominance components C, the chrominance components C 
are supplied to the color memory 3 and the luminance component Y is 
supplied to the specific luminance level information extra section 5, 
respectively. In FIGS. 27 to 29, the luminance component processing system 
composed of the specific luminance level information extract section 5, 
the luminance memory 6, the equi-luminance line tracer 7, and the 
polygonal approximation address list 9 has already been described in 
detail under &lt;Application to luminance component&gt;, so that the description 
thereof is omitted herein. 
In brief, in the luminance component processing system, the equi-luminance 
lines can be extracted by the equi-luminance line tracer 7, and the 
extracted equi-luminance lines are given to the polygonal approximation 
address list 9. 
To retrieve the chrominance components, respective feature point coordinate 
data are supplied from the polygonal approximation address list 9 to a 
chrominance component value read interpolator 58. The chrominance 
component value read interpolator 58 executes the address conversion so as 
to be accessible to color memory 3 and the interpolation calculations for 
the chrominance component values read from the color memory 3, whenever 
the chrominance component values are sampled. The interpolated chrominance 
component values are stored in a chrominance component value list 59. 
In the construction of the image compression side as shown in FIG. 27, 8 
difference in chrominance component value between the current point and 
the preceding point is calculated, and the calculated difference is given 
to a decided section 60. The decide section 60 compares calculated 
difference with a decision criterion allowable value) set in an allowable 
value set section 61, and generates the chrominance component data when 
the difference between the two exceeds the decision criterion. The 
generated data is stored in a chrominance component value list 62, as 
shown in FIGS. 38A and 38B. 
The polygonal approximation address list of the luminance component and the 
chrominance component list obtained as described above are multiplexed by 
the multiplexer 10, and then transmitted to the coder 11. The transmitted 
signals are coded in accordance with a known coding method (e.g., Huffman 
coding), and then transmitted to the decompression side through the 
transmission line 
Further, in the structural example of the image compression side shown in 
FIG. 28, the chrominance component values of the chrominance component 
value list 59 are supplied to a difference convertor 66. The difference 
convertor 66 obtains a difference in chrominance component value between 
the current point and the preceding point on the basis of the correlation 
between the feature points. The obtained difference value is given to 
chrominance component value list 67. The obtained chrominance component 
value is compared with the allowable value by the decide section 60, in 
the same way as with the case of the system shown in FIG. 27. 
Further, in the structural example of the image compression side shown in 
FIG. 29, in the same way as with the case shown in FIG. 27, a difference 
in chrominance component value between the current point and the preceding 
point is calculated, and the calculated difference is given to the decide 
section 60. The decide section 60 compares the calculated difference with 
a decision criterion (an allowable value) set in an allowable value set 
section 61, and generates the chrominance component data when the 
difference between the two exceeds the decision criterion. The generated 
data is stored in the chrominance component value list 62, as shown in 
FIGS. 38A and 38B. Further, the chrominance component values supplied to 
the chrominance component list 62 are further supplied to a vector 
quantizer 68. The vector quantizer 68 regards the chrominance component 
values (u, v) as a vector on the basis of a vector special distribution of 
the chrominance component values included in the chrominance component 
value list, selects a representative vector from a vector group, attaches 
a number to the selected representative vector, and supplies the numbered 
vector to the multiplexer via a vector code list 69. The correspondence 
table between the representative vector numbers and the component values 
is supplied to the multiplexer 10 via a vector code book 70. 
An example of the decompression side shown in FIG. 30 will be explained 
hereinbelow. The decoder 13 can be constructed so as to have a receiver 
function or else to have a receiver section in the front stage thereof. 
The coded data given to the transmission line 12 are decoded by the 
decoder 13. On the other hand, the luminance component thereof are 
supplied to the polygonal approximation address list 220, and the 
chrominance components are supplied to the chrominance component value 
list 75. 
The structural portion from the end point address register 15 to the 
luminance memory 22 processes the compressed and high-efficiency coded 
signals on the basis of specific luminance thresholds. The feature point 
addresses are stored in the two end point address registers 15 and 16, and 
then given to the polygonal interpolation mask forming section 18 as the 
two end-point addresses. The polygonal interpolation mask forming section 
18 calculates the pixel addresses for the linear interpolation between the 
two end-point pixels. The calculated address values are stored in the 
local image memory 17 at a designated luminance. 
After that, when the new feature point address is supplied from the 
polygonal approximation address last 220 to the end point address register 
15, the end point addresses so far stored in the end point address 
register 15 are shifted to the end point address register 16. 0n the other 
hand, the polygonal interpolation mask forming section 18 calculates the 
addresses of the pixels at The two end points for linear interpolation. 
The calculated addresses are stored in the local image memory 17 at a 
designated luminance. Whenever the new feature point address is supplied 
from the polygonal approximation address list 220, the polygonal 
interpolation mask forming section 18 repeats the above-mentioned 
operation to calculate the new interpolation in sequence. The sequential 
calculated address values are stored in the local image memory 17 at a 
designated luminance. 
Further, when a closed curve can be formed completely by the 
above-mentioned interpolation, the polygonal interpolation mask forming 
section 18 forms a mask of a specific luminance level by marking the 
inside of the closed curved lane in the local image memory 17 in bright or 
dark. The above-mentioned process is executed in a plurality of the signal 
processing sections (not shown), to form a predetermined number of masks 
as shown in FIG. 13 by way of example. 
Upon the completion of the mask formation by the polygonal interpolation 
mask forming section 18, the multi-valued decide operator 19 and the 
skeleton luminance decade section 20 operate to allow the luminance plane 
of the local image memory 17 to be multi-valued. In other words, in order 
to improve the quality of the reproduced image, the luminance values are 
reproduced finely in accordance with the luminance interpolation on the 
basis of the relationship between the luminance at the eight adjacent 
pixels. 
With respect to the luminance-undecided region, as shown in FIGS. 19 to 22, 
the skeleton luminance decide section 20 operates in such a way that the 
region central lines (skeleton), that is, a point group equidistance away 
from the region ends are extracted so that the luminance value at the 
pixel on the region central line is set to an intermediate value between 
the two luminance thresholds of two adjacent masks. The decided luminance 
value is stored in the local image memory 17. 
Upon the completion of the above-mentioned luminance decision operation by 
the multi-value decide operator 19 and the skeleton luminance decide 
section 20, the luminance interpolation plane calculate section 21 decides 
the luminance values at the: luminance undecided pixels as [intermediate 
level], [low level], and [high level], as shown in FIG. 12A. Or else, 
after the luminance value at the pixel on the region central line is set 
to an intermediate value between the luminance threshold values of both 
the adjacent masks, the luminance values at the remaining luminance 
undecided pixels are decided by the linear interpolation method, as shown 
in FIGS. 25 and 26, as already explained. 
The luminance signals decompressed by the above-mentioned signal processing 
are stored in the luminance memory 22. The luminance memory 22 operates in 
such a way that the write operation and the read operation are repeated 
alternately in sequence. The luminance signals read from the luminance 
memory 22 are supplied to a video signal generator 24. 
On the other hand, the signal processing section composed of a chrominance 
component value list 75, a feature point address and chrominance component 
list 76, a line generator 77, an interpolation calculation section 78, a 
color memory 79, etc. executes the following processing: First, the signal 
processing section draws lines by connecting a series of feature point 
coordinate points on the equi-luminance line, as shown in FIGS. 35A and 
35B. Further, as shown in FIGS. 36A and 36B, the chrominance component 
values at the end point pixels are determined as the chrominance component 
values at the respective feature points, and the chrominance component 
value at an intermediate pixel between the two end points is decided in 
accordance with the linear interpolation on the basis of both the end 
points. After all the lines between the equi-luminance lines have been 
drawn and the chrominance component values have been decided, as shown in 
FIG. 37, the chrominance component value at the unknown pixel is obtained 
in accordance with the linear interpolation by retrieving the pixels 
having the known chrominance components in the horizontal, vertical and 
oblique directions and by using the obtained values at the adjacent pixels 
and the distances between the them. 
In other words, in the signal processing section from the chrominance 
component value list 75 to the color memory 79, the feature point 
addresses and the chrominance component list are combined by the feature 
point address and chrominance component list 76. The combined addresses 
are given to the line generator 77. Further, after the chrominance 
component values on the equi-luminance line have been developed in the 
color memory 79, the remaining unknown chrominance component values are 
obtained by the interpolation calculation section 78, to restore the 
chrominance components in the color memory 79 
The above-mentioned operation is executed for both the chrominance 
components U and V. The decompressed chrominance signals as processed 
above are stored in the color memory 79. In the color memory 79, two 
memories are so operated as to repeat the write operation and the read 
operation alternately in sequence with respect to each other. The 
luminance signals read from the color memory 79 are supplied to a video 
signal generator 24. 
In the video signal generator 24 generates video signals conforming to a TV 
system of a specific scanning standard on the basis of the luminance 
signals supplied from the luminance memory 22 and the chrominance signals 
supplied from the color memory 79, and supplies the generated video 
signals to a monitor TV, so that a reproduced image can be displayed on a 
display picture of the monitor TV 25. 
As described above in detail, in a multidimensional multi-valued image 
compression and decompression method, according to the present invention, 
of obtaining feature points satisfying a specific condition on an 
equi-luminance line set for each specific luminance value previously 
determined for an image luminance function, and transmitting and recording 
positions and luminance values at the obtained feature points to restore 
an image, since the method comprises, in compression, the steps of: 
tracing at least one pixel contour on the equi-luminance line; and 
shifting specific equi-luminance line passing-point coordinates on the 
traced pixel contour to a central position of the pixel respectively, it 
is possible to solve such a problem that when an image is reproduced on 
the basis of the specific luminance boundaries obtained by tracing the 
image contours in the conventional way, one black pixel is generated for 
each pixel arranged on a specific side of the pixel tracing direction. 
Further, in a multidimensional multi-valued image compression and 
decompression method, according to the present invention, of obtaining 
feature points satisfying a specific condition on an equi-luminance line 
set for each specific luminance value previously determined for an image 
luminance function, and transmitting and recording position and luminance 
values at the obtained feature points to restore an image, since the 
method comprises, in decompression, the steps of: to obtain at least one 
equi-luminance line of a specific luminance value, dividing region whose 
boundary is decided by the equi-luminance line, into a bright region and a 
dark region according to a luminance threshold on the image; and filling 
the divided regions with bright and dark symbols, respectively to form a 
mask representative of the image on the basis of the binary bright and 
dark symbols, it is possible to prevent the luminance order from being 
reversed between the two equi-luminance lines (or equi-luminance planes) 
when the luminance information at the pixels is decided in image 
decompression, so that an abnormal luminance distribution on the image can 
be prevented, without deterioration image quality. 
Further, in the multidimensional multi-valued image compression and 
decompression method, according to the present invention, since the method 
further comprises the steps of: arranging a plurality of masks 
representative of the image in the order of intensities of the luminance 
threshold values corresponding to the respective masks; representing the 
region between two adjacent masks by an intermediate luminance value 
between the luminance threshold values of the two adjacent mask; when the 
relationship between a first luminance value of at least one predetermined 
central pixel in the mask and second luminance values of eight pixels 
around the predetermined central pixel is that: (1) the second luminance 
values ere equal to the first luminance value, the first luminance value 
is determined to be undecided; (2) the second luminance values are not 
higher than the first luminance value, the first luminance value is 
determined to be intermediate value -a, where a is a positive value; (3) 
the second luminance values are not lower than the first luminance value, 
the first luminance value is determined to be the intermediate value +a; 
and (4) the relationship between the first and second luminance values are 
other than the above (1) to (3), the first luminance value is determined 
to be the intermediate value; extracting a central line from the region 
including the pixel of the undecided luminance value, to set at least one 
pixel on central lane of the region to the intermediate luminance value, 
when the pixel luminance information are decided on the image 
decompression side on the basis of the image data markedly compressed on 
the compression side, it is possible to obtain a reproduced image of 
multi-gradations and of high quality, without reversing the luminance 
orders between the equi-luminance line or between the equi-luminance 
plane, that is, without generating an abnormal luminance distribution on 
the image. 
Further, in the multidimensional multi-valued image compression and 
decompression method, according to the present invention, since the method 
further comprises step of: deciding a luminance value at a luminance 
undecided pixel locating away from the central line of the region, in 
accordance with linear interpolation on the basis of two luminance values 
at two luminance-decided pixels locating on both ends of at least one lane 
extending from the luminance-undecided pixel in a predetermined direction 
and on the basis of distances between the luminance-undecided pixel and 
the luminance-decided pixels; or since the method further comprises the 
step of: deciding a luminance value at a luminance-undecided pixel 
locating away from the central line of the region, by averaging the 
luminance values obtained in accordance with a plurality of linear 
interpolations on the basis of respective two luminance values at two 
respective luminance-decided pixels locating on both ends of each of a 
plurality of lines extending from the luminance-undecided pixel in a 
plurality of predetermined directions and on the basis of respective 
distances between the luminance-undecided pixel and the luminance-decided 
pixels along each of a plurality of the extension lines; or the method 
further comprises the step of: deciding a luminance value at a 
luminance-undecided pixel locating away from the central line of the 
region on the basis of plane interpolation values obtained by use of three 
luminance-decided pixels in the vicinity of the luminance-undecided pixel, 
it is possible to obtain a reproduced image of multi-gradations and of 
high quality, without reversing the luminance orders between the 
equi-luminance line or between the equi-luminance plane, that is, without 
generating an abnormal luminance distribution on the image. 
Further, in a color image compression and decompression method according to 
the present invention, since the method comprises the steps of: obtaining 
two-dimensional image addresses and luminance values at a plurality of 
feature points satisfying a specific condition on an equi-luminance lane 
set for each specific luminance value previously determined for luminance 
component for constituting a color image together with chrominance 
components; obtaining the chrominance component values on the basis of the 
chrominance components on the feature points; adding the obtained 
chrominance component values to the two-dimensional address of the 
luminance component at each feature point, as four-dimensional address 
data; setting feature point positions of the chrominance components to 
feature point positions of the luminance component; and transmitting and 
recording the chrominance component values at the positions, to restore an 
image, it is possible to use the luminance component and the chrominance 
components in common, so that it is possible to reduce the amount of the 
information to be transmitted, without deteriorating the image quality 
such as color shearing. In addition, it is possible to further reduce the 
amount of color information by replacing the chrominance components with 
representative values to such an extent as not to exert a harmful 
influence upon the equality of the reproduced image, so that a further 
high efficiency color image compression can be realized. 
Further, in the color image compression and decompression method according 
to the present invention, since the method further comprises the steps of: 
setting an allowable value for the respective chrominance component values 
at each feature points on the equi-luminance line; when the chrominance 
component values at each feature point do not exceed the set allowable 
value, the chrominance component values at the current feature point are 
determined to between same as the chrominance component values at the 
preceding feature point, to reduce the occurrence frequency of the 
chrominance component values, it is possible to use the luminance 
component and chrominance components in common, so that it is possible to 
reduce the amount of the information to be transmitted, without 
deteriorating the image quality such as color shearing. In addition, it is 
possible to further reduce the amount of color information by replacing 
the chrominance components with representative values to such an extent as 
not to exert a harmful influence upon the equality of the reproduced 
image, so that a further high efficiency color image compression can be 
realized.