Image quality prediction apparatus and method, and image quality control apparatus and method

An image quality control apparatus having an image divider, a converter, an image analyzer, an image output property circuit, a quantization method selector, a quantizer and a coder. The image divider divides an input image into a plurality of divided images including a predetermined number of picture elements. The converter converts the divided images into converted coefficients. Then, the image analyzer determines a property of the divided images which is output by the image output property output circuit. A quantization selector selects a quantization method in response to the divided image property that was found by the image analyzer and the image output property output circuit. The quantizer quantizes the conversion coefficients found by the converter in accordance with the quantization methods selected by the quantization selector. A coder then codes the conversion coefficients quantized by the quantizer.

BACKGROUND OF THE INVENTION 
This invention relates to control techniques of the image quality of a 
decoded image and prediction techniques of the image quality of a decoded 
image when image compression is executed. 
In recent years, images have been compressed for reducing the storage media 
capacity or the transmission time. In the description that follows, image 
coding is used in the same meaning as image compression. 
As shown in FIG. 41, input images are compressed, transmitted, and stored 
in a system wherein image input machines such as scanners or image 
generation machines such as computers and image output machines such as 
printers are connected by a network. In recent years, images used in such 
a system have become high definition, colored, and a large capacity, thus 
it becomes important to raise the image compression ratio. 
The image compression systems are classified into reversible and 
non-reversible systems. In the reversible compression system, if an image 
is decompressed after it is compressed, the original image can be restored 
completely. In the non-reversible compression system, a compression ratio 
higher than that in the reversible compression system can be expected, but 
if a compressed image is decompressed, it cannot completely be restored to 
the original image, causing degradation of the image quality. 
Generally, to execute image compression, inversely proportional 
relationship would exist between the compression ratio and the image 
quality under the same coding conditions, because the non-reversible 
compression system realizes a high compression ratio by discarding 
information considered to be hard to affect the sight sense in an image. 
Thus, if the compression ratio is small, a small amount of information is 
discarded and the image quality is good; if the compression ratio is 
enlarged, discarded information is increased and the image quality is 
degraded. 
When image compression is executed, it is desired to maintain a 
predetermined image quality and realize a high compression ratio as much 
as possible. That is, the coded image quality needs to be controlled so as 
to become the maximum permissible image quality. 
Conventional non-reversible compression systems for controlling the coded 
image quality will be discussed below: 
Conventional System 1 
A conventional example of a system for generally controlling the image 
quality for improving the compression ratio of the whole input image will 
be discussed as conventional system 1. 
An input image may change locally in property. If an image is coded by a 
coding system, it contains image portions whose image quality degradation 
is easy to see and those whose image quality degradation is hard to see. 
Considering the image quality of the whole input image, the whole 
compression ratio needs to be lessened to decrease degradation of the 
image portions whose image quality degradation is easy to see. 
Then, an input image is divided into blocks and whether image degradation 
is easy or hard to see is determined for each block. The compression ratio 
is lowered or the quantization step size is narrowed for the block where 
image degradation is easy to see; the compression ratio is raised or the 
quantization step size is widened for the block where image degradation is 
hard to see, whereby the compression ratio of the portion where image 
degradation is hard to see can be raised, thus the whole compression ratio 
can be improved with the image quality made constant. 
For example, a system using DCT (discrete cosine transform) typified by a 
JPEG (joint photographic coding experts group) system as described on 
Maruzen "Multimedia fugouka no kokusai hyoujun 18-43 pages" uses a 
quantization matrix adaptive to the property of an input image block, 
thereby providing a high compression ratio with the same image quality. 
The DCT coding system will be briefly discussed below with reference to 
FIG. 42. 
In FIG. 42, numeral 391 is an input image, numeral 392 is a blocking 
circuit for blocking the input image 391, numeral 393 is an orthogonal 
transformation circuit for orthogonally transforming blocked image 
information, numeral 394 is an orthogonal transformation coefficient, 
numeral 395 is a quantization circuit for quantizing the orthogonal 
transformation coefficient 394, numeral 396 is a coding circuit for coding 
the quantized orthogonal transformation coefficient 394, and numeral 397 
is a code. 
The input image information 391 is separated into rectangular blocks by the 
blocking circuit 392. The blocked image information is orthogonally 
transformed by the orthogonal transformation circuit 393 and the 
orthogonal transformation coefficient 394 is output. The orthogonal 
transformation coefficient 394 is quantized by the quantization circuit 
395 according to a predetermined quantization matrix. The quantized 
orthogonal transformation coefficient is assigned a code by the coding 
circuit 396 and is output as the code 397. 
Since the coding system as shown in FIG. 42 executes the same quantization 
in all blocks, the image quality is degraded in blocks where distortion 
easily appears and visually fruitless information is coded in blocks where 
distortion is hard to appear. 
Then, as described earlier, the conventional system is available which 
executes different quantization for the image portions whose image quality 
degradation is easy to see and those whose image quality degradation is 
hard to see, thereby raising the high compression ratio with the same 
image quality. The conventional system 1 will be discussed with reference 
to FIG. 43. 
Parts identical with those previously described with reference to FIG. 42 
are denoted by the same reference numerals in FIG. 43. In FIG. 43, numeral 
398 is an image analysis circuit for analyzing the property of an image 
block, numeral 399 is the analysis result of the image analysis circuit 
398, numeral 400 is a quantization selection circuit for selecting a 
quantization method based on the analysis result 399, and numeral 401 is a 
quantization method selected by the quantization selection circuit 400. 
The operation of the conventional system 1 will be discussed with FIG. 43. 
Blocked image information is sent to the image analysis circuit 398, which 
then analyzes the image property in each block. Various analysis methods 
are available and specific examples of the analysis methods will be 
described in conventional systems 1-1 and 1-2. Further, the analysis 
result 399 is sent to the quantization selection circuit 400, which then 
selects a quantization method 401. In the DCT coding system, the 
quantization selection circuit 400 selects an optimum quantization matrix 
for the analysis result 399. The selected quantization method 401 is sent 
to the quantization circuit 395, which then uses the quantization method 
401 to execute quantization. The operation of other components is the same 
as the operation in FIG. 42. 
The image quality control system of the conventional system 1 will be 
discussed specifically using two examples of conventional systems 1-1 and 
1-2 with reference to FIG. 43, which picks out the main parts of the 
conventional systems 1-1 and 1-2. 
Conventional System 1-1 
As the conventional system 1-1, techniques disclosed in the Unexamined 
Japanese Patent Application Publication No. Hei 6-165149 will be 
discussed. The conventional system 1-1 determines whether or not each 
input image block is appropriate for coding in the coding system used here 
and if the input image block is appropriate for coding, codes the block at 
a high compression ratio because high image quality can be expected. If 
the input image block is not appropriate for coding, the conventional 
system 1-1 codes the block at a low compression ratio to enhance the image 
quality because low image quality is expected. 
For example, in the conventional system 1-1, the image analysis circuit 398 
in FIG. 43 measures a physical amount 399 indicating easy occurrence of 
mosquito noise for each block. Further, the quantization selection circuit 
400 selects a quantization parameter in response to the physical amount 
399, thereby improving the compression ratio in the same image quality. 
Thus, control is performed so that the code amount increases in a block 
where mosquito noise easily occurs and that the code amount decreases in a 
block where mosquito noise is hard to occur, thereby providing a high 
compression ratio with the same image quality. 
Specifically, in the conventional system 1-1, the image analysis circuit 
398 puts a 3.times.3 pixel window on pixels in a block, finds an average 
value of the absolute values of the gradation level differences between 
contiguous pixels surrounding the center pixel, calculates the number of 
pixels with the ratio between the average value and the range width of 
gradation level signal in the block equal to or less than a predetermined 
threshold value, and determines whether mosquito noise easily occurs or is 
hard to occur depending on whether or not the number of pixels existing in 
the block is equal to or greater than a predetermined number. 
Conventional System 1-2 
As the conventional system 1-2, techniques disclosed in the Unexamined 
Japanese Patent Application Publication No. Hei 7-135671 will be 
discussed. The conventional system 1-2 determines whether or not each 
input image block is visually important and if the input image block is 
visually important, codes the block at a low compression ratio because 
high image quality is desired. If the input image block is not visually 
important, the conventional system 1-2 codes the block at a high 
compression ratio because the image quality may be low. 
In the conventional system 1-2, the image analysis circuit 398 in FIG. 43 
detects the number of pixels 399 with high red saturation in each block. 
Since red information is important for the sight sense of human beings, if 
the number of pixels 399 with high red saturation is large, the 
quantization selection circuit 400 selects such a quantization matrix as 
to lessen the compression ratio of the block; if the number of pixels 399 
with high red saturation is small, the quantization selection circuit 400 
selects such a quantization matrix as to increase the compression ratio of 
the block. 
Specifically, in the conventional system 1-2, the blocking circuit 392 
separates input image signal into blocks of luminance signal, Y signal, 
color difference signal, R-Y signal, and color difference signal, B-Y 
signal. The image analysis circuit 398 determines that a pixel with the 
R-Y signal higher than a predetermined threshold value is high in red 
saturation. Whether or not each pixel is high in red saturation is checked 
and if a block contains a predetermined number of pixels with high red 
saturation or more, the block is set to a superior block. The quantization 
method 401 controls so that the superior block is compressed at a low 
compression ratio. 
Conventional System 1-3 
As the conventional system 1-3, techniques disclosed in U.S. Pat. No. 
5,121,216 will be discussed. 
The conventional system 1-3 determines whether or not when distortion 
caused by coding is added to an input image block, it is easily visually 
perceived. When it is easily perceived, the conventional system 1-3 codes 
the input image block at a low compression ratio; when it is hard to 
perceive, the conventional system 1-3 codes the input image block at a 
high compression ratio, thereby providing a high compression ratio with 
the same image quality on the sight sense of human beings. 
The conventional system 1-3 assumes that a complicated image is hard to 
perceive in distortion, and raises the compression ratio. 
In the conventional system 1, the absolute subjective image quality is not 
described considering that the image quality is relative as to whether or 
not the human being seeing an image satisfies the image quality. 
Conventional System 2 
In the conventional systems, the relationships among input image signals, 
coding system parameters, and image quality are indicated and image input 
and output machines are fixed. In the conventional system 2, image control 
methods when the property of an input machine changes and when the 
property of an output machine changes will be discussed. 
In a conventional system 2-1, an input machine example will be discussed 
and in a conventional system 2-2, an output machine example will be 
discussed. 
Conventional System 2-1 
As the conventional system 2-1, techniques disclosed in the Unexamined 
Japanese Patent Application Publication No. Hei 7-177463 will be 
discussed. The conventional system 2-1 controls a quantization table of 
data compression means based on an f number of aperture means for 
controlling the amount of light incident on image pick-up means. Since the 
f number is an amount representing the property of an image corresponding 
to the physical amount described in the conventional system 1, a 
quantization system can be controlled without analyzing an image. 
The conventional system 2-1 will be discussed with reference to FIG. 44 
which picks out parts related to the invention from the conventional 
system 2-1. In FIG. 44, numeral 411 is an image input through a lens, 
numeral 412 is an aperture for limiting the incident light amount, numeral 
419 is a photoelectric conversion section for converting an input light 
signal into digital data by performing image pick-up, A/D conversion, 
signal processing, etc., numeral 414 is an f number of the aperture 411, 
numeral 415 is a compression ratio selection section for selecting a 
compression ratio based on the f number 414, numeral 413 is a compressing 
section for compressing data at a compression ratio selected by the 
compression ratio selection section 415, and numeral 418 is compressed 
data. 
In FIG. 44, the f number 414 is a physical amount well representing the 
property of input image data and corresponds to the image analysis result 
399 in FIG. 43. Thus, the compression ratio selection section 415 selects 
a quantization table from the f number 414 like the quantization selection 
circuit 400 in FIG. 43. 
Specifically, if the f number is greater than a predetermined number, the 
compression ratio is lowered and if the f number is smaller than the 
predetermined number, the compression ratio is raised, thereby 
guaranteeing the image quality. 
Conventional System 2-2 
As the conventional system 2-2, techniques disclosed in the Unexamined 
Japanese Patent Application Publication No. Hei 6-165148 will be 
discussed. 
To execute image communication, the conventional system 2-2 inquires about 
the display screen size of the associated terminal and codes data in a 
step size to enable sufficient image quality to be provided for the 
display screen size, whereby a high compression rate can be provided 
without degrading the display image quality if the associated terminal has 
a small display screen size. Thus, a large amount of moving information 
can be sent at the same line speed; resultantly, the subjective image 
quality is improved. 
The relationship between the display screen size of the associated terminal 
and the quantization step size appropriate for the size is defined by a 
subjective evaluation experiment. That is, the quantization step size is 
changed for each display screen size and a subjective evaluation value 
called MOS (mean opinion score) is measured, for example, as shown in FIG. 
45. The guaranteed performance of the image quality is determined on the 
MOS scale and the step size which becomes the guaranteed performance is 
found for each display size. In the example in FIG. 45, to transmit data 
to an associated terminal having display size 1, the data is coded in 
quantization step size 1. To transmit data to an associated terminal 
having display size 2, the data is coded in quantization step size 2. To 
transmit data to an associated terminal having display size 3, the data is 
coded in quantization step size 3. 
The MOS, which is one of the subjective evaluation methods of the image 
quality, finds a mean opinion score of the determination results of the 
evaluators as to which of predetermined quality categories each image 
belongs to. 
The conventional system 1 measures the physical amount of an input image 
and codes the input image with a coding parameter adaptive to the physical 
amount. The conventional system 2 measures the state of an input or output 
machine as a physical amount and codes data with a coding parameter 
adaptive to the state. In the conventional systems, the measured physical 
amounts are limited. That is, they are limited only to the measurement 
values of the feature amounts of input images in the conventional system 
1; they are limited only to the measurement values of the feature amounts 
of input or output machines in the conventional system 2. 
However, the factors affecting the actual image quality are not limited. 
They include the input image property, the input machine property, the 
output machine property, etc., as described in the conventional systems. 
Further, the property of the image coding system also affects the image 
quality although not described in the conventional systems. 
In the conventional systems, image coding parameters are given in a 
one-to-one correspondence with input one-dimensional physical amounts; 
other factors affecting the image quality are fixedly considered. Thus, 
the systems are not designed for giving an appropriate coding parameter if 
a plurality of physical amounts are input. 
The image quality depends on the sight sense characteristic of human beings 
and if the image quality affecting factors change independently of each 
other, the image quality changes nonlinearly. Thus, it is difficult to 
predict the image quality for the physical amount for which an actual 
subjective evaluation experiment is not carried out. It is considered that 
for this reason, hitherto, image quality control only with one-dimensional 
physical amounts has been performed. 
Therefore, image quality control considering the properties of various 
image quality affecting factors cannot be performed. 
SUMMARY OF THE INVENTION 
It is therefore an object of the invention to provide an image quality 
control system that can measure the physical amounts of a plurality of 
factors affecting image quality and control the image quality from the 
measured physical amounts. 
The principles of the invention will be first discussed. Hereinafter, the 
image quality felt by a human being who sees the whole image when an 
output image is presented will be referred to as total image quality. 
First, an image quality degradation state is divided into items. The image 
quality degradation items are items that can be measured comparatively 
easily. The total image quality results from various factors and can be 
measure only by subjective evaluation, but the image quality for each 
image quality degradation item can be easily measured. The total image 
quality is determined by totalizing the image quality for each image 
quality degradation item. 
Further, the image quality degradation state is divided into the image 
quality degradation items, whereby various input physical amounts of an 
input image property, an output machine property, an input machine 
property, an image coding system property, etc., are once converted into 
continuous parameters. The space consisting of the parameters can be made 
such a space where the image quality changes continuously. 
Since the image quality in the space is continuous, if it is previously 
measured on a lattice, the image quality by image quality degradation item 
can be measured by interpolation, etc., from the points consisting of the 
parameters. 
Thus, the invention converts various input physical amounts of an input 
image property, an output machine property, an input machine property, an 
image coding system property, etc., into continuous parameters for each 
divided image quality item and predicts the image quality of the image 
quality item from the parameters and the total image quality from the 
image quality for each image quality item. 
Next, the invention will be discussed in more detail. 
According to a first aspect of the invention, there is provided an image 
quality prediction system comprising a plurality of item-by-item image 
quality prediction means and total image quality prediction means for 
determining total evaluated image quality in response to degradation 
degrees predicted by the item-by-item image quality prediction means, 
characterized in that each of the item-by-item image quality prediction 
means comprises input image property space placement means for finding a 
position of an input image property in a space where a degradation degree 
varies continuously for the input image property having relation to an 
image quality degradation evaluation item causing image quality 
degradation of an input image, image coding property space placement means 
for finding a position of an image compression property in a space where a 
degradation degree varies continuously for the image compression property 
affecting the image quality degradation evaluation item causing image 
quality degradation of an image compressed in non-reversible coding, 
output device property space placement means for finding a position of an 
output device property in a space where a degradation degree varies 
continuously for the output device property affecting the image quality 
degradation evaluation item causing image quality degradation of an output 
image, and image quality degradation degree prediction means for 
predicting a degradation degree for the image quality degradation 
evaluation item of the image in response to the position of the input 
image property found by the input image property space placement means, 
the position of the image compression property found by the image coding 
property space placement means, and the position of the output device 
property found by the output device property space placement means, in 
relation to the corresponding image quality degradation evaluation item. 
According to the configuration, various input physical amounts of the input 
image property, the output machine property, the input machine property, 
the image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality of 
the image quality item can be predicted from the parameters and the total 
image quality can be predicted from the image quality for each image 
quality item. 
In the configuration, each of the item-by-item image quality prediction 
means can further include input image property determination means being 
responsive to a parameter of an image input device for controlling an 
image quality of an image input through the image input device for finding 
a property of the input image and the input image property space placement 
means can use the input image property found by the input image property 
determination means. 
Each of the item-by-item image quality prediction means can further include 
input image property determination means for analyzing an input image, 
thereby finding a property of the input image and the input image property 
space placement means can use the input image property found by the input 
image property determination means. 
The total image quality prediction means can determine that the minimum 
value of the degradation degrees predicted by the image quality 
degradation degree prediction means contained in the item-by-item image 
quality prediction means is adopted as total evaluated image quality. 
The total image quality prediction means can determine that the linear sum 
of the degradation degrees predicted by the image quality degradation 
degree prediction means contained in the item-by-item image quality 
prediction means is adopted as total evaluated image quality. 
According to a second aspect of the invention, there is provided an image 
quality control system comprising a plurality of item-by-item image 
quality control method determination means and total image quality control 
means for determining a coding parameter in image compression to 
accomplish desired image quality in response to relationship between 
degradation degrees determined by the item-by-item image quality control 
method determination means and coding parameters, characterized in that 
each of the item-by-item image quality control method determination means 
comprises input image property space placement means for finding a 
position of an input image property in a space where a degradation degree 
varies continuously for the input image property having relation to an 
image quality degradation evaluation item causing image quality 
degradation of an input image, image coding property space placement means 
for finding a position of an image compression property in a space where a 
degradation degree varies continuously for the image compression property 
affecting the image quality degradation evaluation item causing image 
quality degradation of an image compressed in non-reversible coding, 
output device property space placement means for finding a position of an 
output device property in a space where a degradation degree varies 
continuously for the output device property affecting the image quality 
degradation evaluation item causing image quality degradation of an output 
image, and image quality control method determination means for 
determining relationship between a degradation degree for the image 
quality degradation evaluation item and a coding parameter in image 
compression in response to the position of the input image quality found 
by the input image property space placement means, the position of the 
image compression property found by the image coding property space 
placement means, and the position of the output device property found by 
the output device property space placement means, in relation to the 
corresponding image quality degradation evaluation item. 
In the configuration, various input physical amounts of the input image 
property, the output machine property, the input machine property, the 
image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality 
control method of the image quality item can be determined from the 
parameters and the total image quality can be controlled by the determined 
image quality control methods. 
In the configuration, each of the item-by-item image quality control method 
determination means can further include input image property determination 
means being responsive to a parameter of an image input device for 
controlling an image quality of an image input through the image input 
device for finding a property of the input image and the input image 
property space placement means can use the input image property found by 
the input image property determination means. 
Each of the item-by-item image quality control method determination means 
can further include input image property determination means for analyzing 
an input image, thereby finding a property of the input image and the 
input image property space placement means can use the input image 
property found by the input image property determination means. 
The total image quality control means can determine a coding parameter to 
accomplish desired image quality and provide the minimum compression ratio 
based on the relationship between the degradation degree and coding 
parameter in image compression determined by the image quality control 
method determination means contained in each of the item-by-item image 
quality control method determination means. 
According to a third aspect of the invention, there is provided an image 
quality control system comprising image division means for dividing an 
input image into given constant areas, conversion means for converting the 
image divided by the image division means and finding a conversion 
coefficient, image analysis means for finding a property of the image 
divided by the image division means, image output property output means 
for outputting a property of image output means, quantization method 
selection means for selecting a quantization method in response to the 
divided image property found by the image analysis means and the image 
output means property output by the image output property output means, 
quantization means for quantizing the conversion coefficient found by the 
conversion means by the quantization method selected by the quantization 
method selection means, and coding means for coding the conversion 
coefficient quantized by the quantization means. 
According to the configuration, the quantization method is determined in 
response to the divided image property and the image output means 
property, so that fine image quality control is enabled. 
In the configuration, the image output means property output by the image 
output property output means can include the effective number of gray 
levels of the image output means. 
The image output means property output by the image output property output 
means can include the output frequency characteristic of the image output 
means. 
The divided image property found by the image analysis means can include 
the line width in the divided image. 
The divided image property found by the image analysis means can include a 
power distribution for each frequency of the divided image. 
According to a fourth aspect of the invention, there is provided an image 
quality control system comprising image division means for dividing an 
input image into given constant areas, conversion means for converting the 
image divided by the image division means and finding a conversion 
coefficient, image analysis means for finding a property of the image 
divided by the image division means, image coding property output means 
for outputting a property of a quantization method and a property of the 
conversion means, quantization method selection means for selecting a 
quantization method in response to the divided image property found by the 
image analysis means and the quantization method property and the 
conversion means property output by the image coding property output 
means, quantization means for quantizing the conversion coefficient found 
by the conversion means by the quantization method selected by the 
quantization method selection means, and coding means for coding the 
conversion coefficient quantized by the quantization means. 
In the configuration, the quantization method is determined in response to 
the divided image property, quantization method property, and the property 
of the conversion means for generating a conversion coefficient, so that 
finer image quality control is enabled. 
In the configuration, the image output means property output by the image 
output property output means can include the effective number of gray 
levels of the image output means. 
The conversion means property output by the image coding property output 
means can include a property related to discrete cosine transform. 
The conversion means property output by the image coding property output 
means can include a property related to conversion based on prediction. 
The image output means property output by the image output property output 
means can include the output frequency characteristic of the image output 
means. 
The divided image property found by the image analysis means can include 
the line width in the divided image. 
The divided image property found by the image analysis means can include a 
power distribution for each frequency of the divided image. 
According to a fifth aspect of the invention, there is provided an image 
quality prediction system comprising a plurality of item-by-item image 
quality prediction means each comprising input image property input means 
for inputting a property of an input image affecting an image quality 
degradation evaluation item causing image quality degradation of an input 
image, image coding property input means for inputting a property of image 
compression affecting an image quality degradation evaluation item causing 
image quality degradation of an image compressed in non-reversible coding, 
output device property input means for inputting a property of an output 
device affecting an image quality degradation evaluation item causing 
image quality degradation of an output image, and image quality 
degradation degree prediction means for predicting a degradation degree 
for the image quality degradation evaluation item of the image in response 
to the input image property input by the input image property input means, 
the image compression property input by the image coding property input 
means, and the output device property input by the output device property 
input means, for a plurality of image quality degradation evaluation 
items, and total image quality prediction means for determining total 
evaluated image quality in response to evaluated image quality predicted 
by the image quality degradation degree prediction means contained in each 
of the item-by-item image quality prediction means. 
Also in the configuration, various input physical amounts of the input 
image property, the output machine property, the input machine property, 
the image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality of 
the image quality item can be predicted from the parameters and the total 
image quality can be predicted from the image quality for each image 
quality item. 
In the configuration, each of the item-by-item image quality prediction 
means can further include a memory for storing evaluated image quality 
determined by the input image property, the image compression property, 
and the output device property and previously found by an evaluation 
experiment for each image quality degradation evaluation item and the 
image quality degradation degree prediction means can read the evaluated 
image quality stored in the memory by using the input image property input 
by the input image property input means, the image compression property 
input by the image coding property input means, and the output device 
property found by the output device property input means. 
If evaluated image quality corresponding to the input image property input 
by the input image property input means, the image compression property 
input by the image coding property input means, and the output device 
property found by the output device property input means is not stored in 
the memory, the image quality degradation degree prediction means can use 
values close to the input image property input by the input image property 
input means, the image compression property input by the image coding 
property input means, and the output device property found by the output 
device property input means to read relationship between the evaluated 
image quality stored in the memory and coding parameter in image 
compression. 
According to a sixth aspect of the invention, there is provided an image 
quality control system comprising a plurality of item-by-item image 
quality control method determination means each comprising input image 
property input means for inputting a property of an input image affecting 
an image quality degradation evaluation item causing image quality 
degradation of an input image, image coding property input means for 
inputting a property of image compression affecting an image quality 
degradation evaluation item causing image quality degradation of an image 
compressed in non-reversible coding, output device property input means 
for inputting a property of an output device affecting an image quality 
degradation evaluation item causing image quality degradation of an output 
image, and image quality control method determination means for 
determining relationship between a degradation degree for the image 
quality degradation evaluation item and coding parameter in image 
compression in response to the input image property input by the input 
image property input means, the image compression property input by the 
image coding property input means, and the output device property input by 
the output device property input means, for a plurality of image quality 
degradation evaluation items, desired image quality input means for an 
operator to enter desired image quality, and total image quality control 
means for determining a coding parameter to accomplish the desired image 
quality entered through the desired image quality input means in response 
to the relationship between the evaluated image quality and coding 
parameter determined by the image quality control method determination 
means contained in each of the item-by-item image quality control method 
determination means. 
Also in the configuration, various input physical amounts of the input 
image property, the output machine property, the input machine property, 
the image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality 
control method of the image quality item can be determined from the 
parameters and the total image quality can be controlled by the determined 
image quality control methods. 
In the configuration, each of the item-by-item image quality control method 
determination means may further include a memory for storing relationship 
between evaluated image quality determined by the input image property, 
the image compression property, and the output device property and 
previously found by an evaluation experiment for each image quality 
degradation evaluation item and coding parameter in image compression and 
the image quality control method determination means may read the 
relationship between the evaluated image quality and coding parameter in 
image compression stored in the memory by using the input image property 
input by the input image property input means, the image compression 
property input by the image coding property input means, and the output 
device property found by the output device property input means. 
If the relationship between the evaluated image quality and coding 
parameter in image compression corresponding to the input image property 
input by the input image property input means, the image compression 
property input by the image coding property input means, and the output 
device property found by the output device property input means is not 
stored in the memory, the image quality control method determination means 
may use values close to the input image property input by the input image 
property input means, the image compression property input by the image 
coding property input means, and the output device property found by the 
output device property input means to read the relationship between the 
evaluated image quality and coding parameter in image compression stored 
in the memory. 
The image quality control system may further include input image analysis 
means for analyzing an input image and input image quality effect degree 
calculation means for calculating an effect degree for an image quality 
degradation evaluation item in response to the analysis result of the 
input image analysis means, and the input image property input means may 
input the effect degree calculated by the input image quality effect 
degree calculation means. 
The input image analysis means can include as a property of the input image 
to be analyzed, any one or more of the number of pixel value types of the 
input image, pixel value change of peripheral pixels, power of 
low-frequency and high-frequency signals, and image quality when a given 
image is coded by a given coding system and the resultant image is output 
on a given output device. 
The output device property input means may include as a property of an 
output device, any one or more of resolution of the output device, the 
number of gray levels, a frequency transfer characteristic, dot form, dot 
print accuracy, the number of halftone dot lines, halftone dot form, tone 
curve, contrast, and image quality when a given image is coded by a given 
coding system. 
The image coding property input means may include as a property of image 
compression, any one or more of a blocking technique, a quantization 
characteristic, a frequency transfer characteristic, a subsampling 
technique, an interpolation technique, and a conversion technique when the 
image compression is executed, and image quality when a given image is 
output by a given output device. 
The input image property input means may include as a property of an input 
image, any one or more of image quality when the input image is coded by a 
given coding system and the resultant image is output on a given output 
device and input camera aperture information, pixel density, pixel size, 
and the number of quantization bits of image input device property. 
According to a seventh embodiment of the invention, there is provided an 
image quality control system comprising a plurality of item-by-item image 
quality control method determination means each comprising input image 
property input means for inputting a property of an input image affecting 
an image quality degradation evaluation item causing image quality 
degradation of an input image, image coding property input means for 
inputting a property of image compression affecting an image quality 
degradation evaluation item causing image quality degradation of an image 
compressed in non-reversible coding, output device information input means 
for inputting information concerning an output device, and image quality 
control method determination means for determining relationship between a 
degradation degree for the image quality degradation evaluation item and 
coding parameter in image compression in response to the input image 
property input by the input image property input means, the image 
compression property input by the image coding property input means, and 
the output device information input by the output device information input 
means, for a plurality of image quality degradation evaluation items, 
target image quality input means for inputting target image quality, and 
total image quality control means for determining a coding parameter to 
accomplish the target image quality input by the target image quality 
input means in response to the relationship between the evaluated image 
quality and coding parameter determined by the image quality control 
method determination means contained in each of the item-by-item image 
quality control method determination means. 
Also in the configuration, various input physical amounts of the input 
image property, the input machine property, the image coding system 
property, etc., are converted into continuous parameters for each divided 
image quality item and the image quality control method of the image 
quality item can be determined from the parameters and output device 
information and the total image quality can be controlled by the 
determined image quality control methods. 
The output device information is the output device type or output device 
identification information, for example. The output device types contain a 
xerographic printer, a silver salt photo printer, an offset printer, a CRT 
display, an LCD display, etc. The output device identification information 
contains the device names, the device numbers assigned by the 
manufacturers. 
According to an eighth aspect of the invention, there is provided an image 
quality prediction method comprising step A of finding a position of an 
input image property in a space where a degradation degree varies 
continuously for the input image property having relation to an image 
quality degradation evaluation item causing image quality degradation of 
an input image, step B of finding a position of an image compression 
property in a space where a degradation degree varies continuously for the 
image compression property affecting an image quality degradation 
evaluation item causing image quality degradation of an image compressed 
in non-reversible coding, step C of finding a position of an output device 
property in a space where a degradation degree varies continuously for the 
output device property affecting an image quality degradation evaluation 
item causing image quality degradation of an output image, and step D of 
predicting a degradation degree for the image quality degradation 
evaluation item of the image in response to the position of the input 
image property found in the step A, the position of the image compression 
property found in the step B, and the position of the output device 
property found in the step C, for a plurality of image quality degradation 
evaluation items, and step E of determining total evaluated image quality 
in response to the evaluated image quality predicted for each of the image 
quality degradation evaluation items. 
Also in the configuration, various input physical amounts of the input 
image property, the output machine property, the input machine property, 
the image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality of 
the image quality item can be predicted from the parameters and the total 
image quality can be predicted from the image quality for each image 
quality item. 
According to a ninth aspect of the invention, there is provided an image 
quality control method comprising step A of finding a position of an input 
image property in a space where a degradation degree varies continuously 
for the input image property having relation to an image quality 
degradation evaluation item causing image quality degradation of an input 
image, step B of finding a position of an image compression property in a 
space where a degradation degree varies continuously for the image 
compression property affecting an image quality degradation evaluation 
item causing image quality degradation of an image compressed in 
non-reversible coding, step C of finding a position of an output device 
property in a space where a degradation degree varies continuously for the 
output device property affecting an image quality degradation evaluation 
item causing image quality degradation of an output image, and step D of 
determining relationship between a degradation degree for the image 
quality degradation evaluation item and coding parameter in image 
compression in response to the position of the input image property found 
in the step A, the position of the image compression property found in the 
step B, and the position of the output device property found in the step 
C, for a plurality of image quality degradation evaluation items, and step 
E of determining a coding parameter to accomplish desired image quality in 
response to the relationship between the evaluated image quality and 
coding parameter determined for each of the image quality degradation 
evaluation items. 
Also in the configuration, various input physical amounts of the input 
image property, the output machine property, the input machine property, 
the image coding system property, etc., are converted into continuous 
parameters for each divided image quality item and the image quality 
control method of the image quality item can be determined from the 
parameters and the total image quality can be controlled by the determined 
image quality control methods. 
In the description made so far, the image quality or image quality 
degradation for the image quality evaluation or image quality degradation 
evaluation items of an image also includes objective evaluation image 
quality values that can be measured as physical amounts in addition to the 
values of the subjective evaluation image quality felt by the observer for 
the blur, edge business, etc., of the image.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Referring now to the accompanying drawings, there are shown preferred 
embodiments of the invention. 
Embodiment 1 
The image quality generally depends on the properties of an input image, an 
image output device, and an image coding system. An image quality 
prediction system of a first embodiment of the invention predicts the 
image quality on a space converted into values independent of the 
properties. To provide the space independent of input/output of an image 
or the coding system, the image quality is divided into image quality 
degradation items. The image quality or image quality degradation for the 
image quality evaluation or image quality degradation evaluation items of 
an image also includes objective evaluation image quality values that can 
be measured as physical amounts in addition to the values of the 
subjective evaluation image quality felt by the observer for the blur, 
edge business, etc., of the image. 
The first embodiment will be discussed with reference to FIGS. 1, 2, and 3. 
FIG. 1 shows the image quality prediction system of the first embodiment as 
a whole. In FIG. 1, the image quality prediction system consists of a 
plurality of item-by-item image quality prediction means 101a-101n and 
total image quality prediction means 102. The corresponding input image 
property 103, image output property 104, and image coding property 105 are 
input to each of the item-by-item image quality prediction means 
101a-101n, each of which then predicts the image quality by item based on 
the corresponding input image property 103, image output property 104, and 
image coding property 105 and outputs item-by-item predicted image quality 
106a-106n. The total image quality prediction means 102 outputs total 
image quality 107 based on the item-by-item predicted image qualities 
106a-106n. 
FIG. 2 shows the configuration of the item-by-item image quality prediction 
means 101 (101a-101n). In the figure, the item-by-item image quality 
prediction means 101 consists of input image property space placement 
means 108, image output property space placement means 109, image coding 
property space placement means 110, and image quality prediction means 
111. The input image property space placement means 108, the image output 
property space placement means 109, and the image coding property space 
placement means 110 calculate and output an input image property 
intra-space position 112, an image output property intra-space position 
113, and an image coding property intra-space position 114, respectively, 
based on the input image property 103, the image output property 104, and 
the image coding property 105. The image quality prediction means 111 
outputs item-by-item predicted image quality 106 based on the input image 
property intra-space position 112, the image output property intra-space 
position 113, and the image coding property intra-space position 114. 
FIG. 3 illustrates an input image property space, an image output property 
space, and an image coding property space by image quality degradation 
item. For simplicity, FIG. 3 assumes that each space is one dimension. 
In FIGS. 1 and 2, the input image property 103 indicating easy occurrence 
of image degradation for each of image degradation items of block 
distortion, blur, etc., is input to the input image property space 
placement means 108, which then places the input image property in the 
point of the input image property in FIG. 3 and outputs the input image 
property intra-space position 112. The input image property 103 is the 
edge amount, signal power at one frequency, etc., for example. 
Likewise, the image output property 104 and the image coding property 105 
are also placed in the image output property space and the image coding 
property space as easy occurrence of image quality degradation with 
respect to the sight sense for each image degradation item, and the image 
output property intra-space position 113 and the image coding property 
intra-space position 114 are output. The image output property 104 is 
printer output resolution, etc., for example. The image coding property 
105 is a DCT coefficient quantization matrix, etc., for example. 
The input image property space, the image output property space, and the 
image coding property space are taken so as to be contiguous with each 
other. 
Assuming that the number of dimensions of the input image property space is 
A, that that of the image output property space is B, and that that of the 
image coding property space is C, an (A+B+C) dimensional space with the 
spaces as partial spaces can be put. The image quality can be measured for 
each point in the space. Hereinafter, the (A+B+C) dimensional space will 
be called item-by-item image quality space. 
The image quality prediction means 111 predicts image quality values by 
item and outputs item-by-item predicted image quality 106. That is, the 
image quality on the lattice points in the item-by-item image quality 
space in FIG. 3 is previously measured. The input image property space, 
the image output property space, and the image coding property space, 
which are defined as easy occurrence of image degradation of the 
corresponding image degradation items, do not depend on the input image, 
image output means, or image coding system. Since the input image property 
space, the image output property space, and the image coding property 
space are contiguous with each other, the image quality in the 
item-by-item image quality space is assumed to be continuous, and the 
image quality evaluation values on the lattice points previously measured 
are used to predict the image quality. 
The image quality is classified into items, whereby the number of 
dimensions of the item-by-item image quality space can be decreased. 
The item-by-item predicted image quality 106 thus provided is input to the 
total image quality prediction means 102 for finding the total image 
quality 107. 
The property of an input image may be determined by the property of an 
image input machine, such as aperture information of an input camera. It 
may be found by analyzing the input image. For example, the edge amount, 
signal power at a predetermined frequency, etc., may be analyzed. 
To previously measure the image quality in the item-by-item image quality 
space, the number of measurement points should be lessened as much as 
possible. Thus, the number of dimensions of each of the input image 
property space, the image output property space, and the image coding 
property space should be lessened as much as possible. 
The image degradation items should be finely divided so that the number of 
dimensions of each of the input image property space, the image output 
property space, and the image coding property space is set to one. In 
doing so, previous subjective evaluation is facilitated. For example, the 
image degradation items are measured for each density value of an image, 
whereby the number of dimensions of each space can be decreased. 
The total image quality prediction means 102 can use the minimum value or 
the linear sum of the item-by-item predicted image qualities 106 as the 
total image quality 107. 
The image output property also includes image output resolution and the 
number of gray levels. Information concerning an output device, such as 
the type or identification information of the output device, may be 
entered as the image output property to indirectly specify the image 
output property. The output device types contain a xerographic printer, a 
silver salt photo printer, an offset printer, a CRT display, an LCD 
display, etc. The output device identification information contains the 
device names, the device numbers assigned by the manufacturers. 
Embodiment 2 
Next, a second embodiment of the invention will be discussed. In the first 
embodiment, the image quality of an output image is predicted from the 
input image property, the image output property, and the image coding 
property. In the second embodiment, the relationship between the image 
quality of an output image and image coding parameters is predicted from 
the input image property, the image output property, and the image coding 
property from which image coding parameters are removed, thereby enabling 
coding in the specified image quality. 
The second embodiment will be discussed with reference to FIGS. 4, 5, 6, 
and 7. 
FIG. 4 shows an image quality control system of the second embodiment as a 
whole. Parts identical with or similar to those previously described with 
reference to FIG. 1 are denoted by the same reference numerals in FIG. 4. 
In FIG. 4, the image quality control system consists of a plurality of 
item-by-item image quality control method determination means 121a-121n 
and total image quality control means 122. The corresponding input image 
property 103, image output property 304, and image coding property 105 
(from which coding parameters are removed) are input to each of the 
item-by-item image quality control method determination means 121a-121n, 
each of which then outputs image control method 123a-123n based on the 
corresponding input image property 103, image output property 104, and 
image coding property 105. The total image quality control means 122 
determines and outputs a total coding parameter 124 based on the image 
control methods 123a-123n. 
FIG. 5 shows the configuration of the item-by-item image quality control 
method determination means 121 (121a-121n). Parts identical with or 
similar to those previously described with reference to FIG. 2 are denoted 
by the same reference numerals in FIG. 5. In FIG. 5, the item-by-item 
image quality control method determination means 121 consists of input 
image property space placement means 108, image output property space 
placement means 109, image coding property space placement means 110, and 
image quality control method determination means 125. The input image 
property space placement means 108, the image output property space 
placement means 109, and the image coding property space placement means 
110 calculate and output an input image property intra-space position 112, 
an image output property intra-space position 113, and an image coding 
property intra-space position 114, respectively, based on the input image 
property 103, the image output property 104, and the image coding property 
105. The image quality control method determination means 121 outputs 
image quality control method 123 based on the input image property 
intra-space position 112, the image output property intra-space position 
113, and the image coding property intra-space position 114. 
As shown in FIG. 6, the image coding property from which some image coding 
parameters are removed, the input image property, and the image output 
property 104 are placed in an item-by-item image quality space. Here, the 
item-by-item image quality space shows the relationship between the coding 
parameters and the image quality, as shown in FIG. 7. 
The total image quality control means 122 finds a coding parameter to 
enable desired image quality to be provided for each image quality 
degradation item and finds a coding parameter to enable desired image 
quality to be provided on the whole. 
Also in the second embodiment, the property of an input image may be 
determined by the property of an image input machine, such as aperture 
information of an input camera. It may be found by analyzing the input 
image. For example, the edge amount, signal power at a predetermined 
frequency, etc., may be analyzed. 
To previously measure the image quality in the item-by-item image quality 
space, the number of measurement points should be lessened as much as 
possible. Thus, the number of dimensions of each of the input image 
property space, the image output property space, and the image coding 
property space should be lessened as much as possible. 
The image degradation items should be finely divided so that the number of 
dimensions of each of the input image property space, the image output 
property space, and the image coding property space is set to one. In 
doing so, previous subjective evaluation is facilitated. For example, the 
image degradation items are measured for each density value of an image, 
whereby the number of dimensions of each space can be decreased. 
The total image quality control means 122 may adopt a coding parameter for 
minimizing the compression ratio among the coding parameters satisfying 
the desired image quality as the final coding parameter based on the 
relationship between the image quality and the coding parameters. 
The image output property also includes image output resolution and the 
number of gray levels. Information concerning an output device, such as 
the type or identification information of the output device, may be 
entered as the image output property to indirectly specify the image 
output property. The output device types contain a xerographic printer, a 
silver salt photo printer, an offset printer, a CRT display, an LCD 
display, etc. The output device identification information contains the 
device names, the device numbers assigned by the manufacturers. 
Embodiment 3 
A third embodiment of the invention provides an image coding system that 
can control the image quality when an image output device changes. 
FIG. 8 shows the configuration of the third embodiment. In the figure, 
numeral 131 is an input image, numeral 132 is image division means for 
dividing the input image 131 into blocks, numeral 133 is conversion means 
for converting the divided image, numeral 134 is a conversion coefficient, 
numeral 135 is quantization means for quantizing the conversion 
coefficient 134, numeral 136 is coding means for coding the quantized 
conversion coefficient, numeral 137 is a code, numeral 138 is image 
analysis means for analyzing the divided image and outputting an input 
image property, numeral 139 is an input image property, numeral 140 is 
quantization selection means, numeral 141 is a selected quantization 
method, numeral 142 is image output property output means, and numeral 143 
is an image output property. 
The input image 131 is divided into blocks by the image division means 132 
and converted into the conversion coefficient 134 by the conversion means 
133. Further, the image divided into blocks is analyzed by the image 
analysis means 138 and sent to the quantization selection means 140 as the 
input image property 139. Further, the image output property output means 
142 outputs the image output property 143 to the quantization selection 
means 140. 
The quantization selection means 140 consists of item-by-item image quality 
control method determination means 121 and total image quality control 
means 122 in FIG. 4 and selects a quantization method of a coding 
parameter providing predetermined image quality. 
The quantization means 135 quantizes the conversion coefficient 134 by the 
selected quantization method and the coding means 136 outputs code 137. 
The conversion means 133 can adopt a prediction method of predicting a 
coding pixel value from discrete cosine transform or pixel values in the 
proximity, for example. 
The effective number of gray levels or output frequency characteristic can 
be used as the image output property 143. 
The image analysis means 138 detects the line width and power spectrum of 
an input image, for example. 
Embodiment 4 
A fourth embodiment of the invention provides an image coding system that 
can control the image quality when the image coding property changes. FIG. 
9 shows the configuration of the fourth embodiment. In the embodiment, 
image coding property output means 144 outputs an image coding property 
145 and a quantization method is determined based on the image coding 
property 145. The fourth embodiment is the same as the third embodiment 
except that the image output property output means 142 in FIG. 8 becomes 
the image coding property output means 144 or that the image output 
property 143 in FIG. 8 changes to the image coding property 145, and will 
not be discussed again in detail. 
Embodiment 5 
A fifth embodiment of the invention provides a specific configuration of 
the image prediction means 111 of the first embodiment. FIG. 10 shows a 
configuration example of the fifth embodiment. In the figure, numeral 146 
is address calculation means, numeral 147 is an address, and numeral 148 
is image storage means. Image quality is stored in the image storage means 
148. The address 147 at which the image quality is stored can be found 
from an input image property intra-space position 112, an image output 
property intra-space position 113, and an image coding property 
intra-space position 114. The address calculation means 146 calculates the 
address 147 and sends it to the image storage means 148, which then 
outputs predicted image quality 106 corresponding to the address 147. 
Embodiment 6 
A sixth embodiment of the invention provides a specific configuration of 
the image quality control method determination means 125 of the second 
embodiment. FIG. 11 shows a configuration example of the sixth embodiment. 
In the figure, numeral 149 is image quality control method storage means. 
Although the image quality storage means 148 in FIG. 10 outputs the 
predicted image quality 106, the image quality control method storage 
means 149 in FIG. 11 outputs an image quality control method 123. 
Embodiment 7 
Next, an image quality prediction system of a seventh embodiment of the 
invention will be discussed. FIG. 12 shows the image quality prediction 
system of the seventh embodiment as a whole. FIG. 13 shows the 
configuration of item-by-item image quality prediction means 162. In FIGS. 
12 and 13, numeral 152 is input image property input means, numeral 153 is 
an input image property, numeral 155 is image output property input means, 
numeral 156 is an image output property, numeral 158 is image coding 
property input means, numeral 159 is an image coding property, numeral 160 
is image quality prediction means, numeral 161 is predicted item-by-item 
image quality, numeral 162 is item-by-item image quality prediction means, 
numeral 163 is total image quality prediction means, and numeral 164 is 
predicted image quality. 
FIG. 14 shows the configuration of the image quality prediction means. In 
the figure, numeral 171 is image quality storage means, numeral 172 is 
stored image quality, numeral 173 is an address in the image quality 
storage means, and numeral 174 is image quality calculation means. 
FIG. 15 shows the configuration of the input image property input means 
152. In the figure, numeral 153 is an input image property, numeral 181 is 
an input image, numeral 182 is image analysis means, numeral 183 is an 
input image analysis result, and numeral 184 is input image quality effect 
degree calculation means. 
FIG. 16 shows the configuration of the image output property input means 
155. In the figure, numeral 156 is an image output property, numeral 191 
is an image output device state, numeral 192 is image output image quality 
effect degree storage means, numeral 193 is an image output image quality 
effect degree, and numeral 194 is image output image quality effect degree 
calculation means. 
FIG. 17 shows the configuration of the image coding property input means 
158. In the figure, numeral 159 is image coding property, numeral 201 is 
an image coding parameter state, numeral 202 is image coding image quality 
effect degree storage means, numeral 203 is an image coding image quality 
effect degree, and numeral 204 is image coding image quality effect degree 
calculation means. 
Referring to FIG. 13, the input image property 153 is input through the 
input image property input means 152 to the image quality prediction means 
160. The image output property 156 is input through the image output 
property input means 155 to the image quality prediction means 160. 
Further, the image coding property 159 is input through the image coding 
property input means 158 to the image quality prediction means 160. The 
image quality prediction means 160 predicts image quality by image quality 
degradation item from the input image property 153, the image output 
property 156, and the image coding property 159 and outputs the 
item-by-item image quality 161. The total image quality prediction means 
163 predicts total predicted image quality 164 based on one or more 
item-by-item image qualities. 
In the input image property input means 152 in FIG. 15, the image analysis 
means 182 analyzes the input image 181 and enters the input image analysis 
result 183 in the input image quality effect degree calculation means 184, 
which then outputs the input image quality effect degree as the input 
image property 153. 
In the image output property input means 155 in FIG. 16, the image output 
image quality effect degree storage means 192 enters the image output 
image quality effect degree 193 in the image output image quality effect 
degree calculation means 194 once or more than once in accordance with the 
input image output device state, and the image output image quality effect 
degree calculation means 194 outputs a new calculated image output image 
quality effect degree from the one or more image output image quality 
effect degrees as the image output property 156. 
In the image coding property input means 158 in FIG. 17, the image coding 
image quality effect degree storage means 202 enters the image coding 
image quality effect degree 203 in the image coding image quality effect 
degree calculation means 204 once or more than once in accordance with the 
input image coding parameter state 201, and the image coding image quality 
effect degree calculation means 204 outputs a new calculated image coding 
image quality effect degree from one or more image coding image quality 
effect degrees as the image coding property 159. 
In the image quality prediction means 160 in FIG. 14, the image quality 
calculation means 174 calculates one or more addresses 173 seeming to be 
the nearest from the input image property 153, the image output property 
156, and the image coding property 159 and sends the one or more 
calculated addresses to the image quality storage means 171, which then 
returns the image quality 172 to the image quality calculation means 174, 
which then calculates the image quality 161 based on the received image 
quality 172. 
Embodiment 8 
Next, an image quality control system of an eighth embodiment of the 
invention will be discussed. FIG. 18 shows the image quality control 
system of the eighth embodiment as a whole. FIG. 19 shows the 
configuration of item-by-item image quality control method determination 
means 222. In FIGS. 18 and 19, numeral 152 is input image property input 
means, numeral 153 is an input image property, numeral 155 is image output 
property input means, numeral 156 is an image output property, numeral 158 
is image coding property input means, numeral 159 is an image coding 
property, numeral 211 is desired image quality input means, numeral 212 is 
desired image quality, numeral 220 is image quality control method 
determination means, numeral 221 is an item-by-item image quality control 
method, numeral 222 is item-by-item image quality control method 
determination means, numeral 223 is total image quality control means, and 
numeral 224 is a coding parameter. 
FIG. 20 shows the configuration of the image quality control method 
determination means 220. In the figure, numeral 231 is image quality 
control method storage means, numeral 232 is a stored image quality 
control method, numeral 233 is an address in the image quality control 
method storage means 232, and numeral 234 is image quality control method 
calculation means. 
FIG. 21 shows the configuration of the input image property input means 
152. In the figure, numeral 152 is input image property input means, 
numeral 153 is an input image property, numeral 241 is an image input 
device state, numeral 242 is input image quality effect degree storage 
means, numeral 243 is an input image quality effect degree, and numeral 
244 is input image quality effect degree calculation means. 
Referring to FIGS. 18 and 19, the input image property 153 is input through 
the input image property input means 152 to the image quality control 
method determination means 220. The image output property 156 is input 
through the image output property input means 155 to the image quality 
control method determination means 220. Further, the image coding property 
159 is input through the image coding property input means 158 to the 
image quality control method determination means 220. The image quality 
control method determination means 220 determines an image quality control 
method by image quality degradation item from the input image property 
153, the image output property 156, and the image coding property 159 and 
outputs the item-by-item image quality control method 221. The total image 
quality control means 223 determines a coding parameter satisfying the 
desired image quality in each item-by-item image quality from each 
item-by-item image quality control method and desired image quality 212 
and further determines a coding parameter 164 satisfying the total image 
quality. 
In the input image property input means 152 in FIG. 21, the input image 
quality effect degree storage means 242 enters the input image quality 
effect degree 243 in the input image quality effect degree calculation 
means 244 once or more than once in accordance with the input image input 
device state 241, and the input image quality effect degree calculation 
means 244 outputs a new calculated input image quality effect degree from 
the one or more input image quality effect degrees as the image output 
property 156. 
The image output property input means 155 and the image coding property 
input means 158 are similar to those of the seventh embodiment. 
In the image quality control method determination means 220 in FIG. 20, the 
image quality control method calculation means 234 calculates one or more 
addresses 233 seeming to be the nearest from the input image property 153, 
the image output property 156, and the image coding property 159 and sends 
the one or more calculated addresses to the image quality control method 
storage means 231, which then returns the image quality control method 232 
to the image quality control method calculation means 234, which then 
determines the image quality control method 221 based on the received 
image quality control method 232. 
Embodiment 9 
Next, a ninth embodiment of the invention will be discussed. The ninth 
embodiment uses JPEG as a coding system and analyzes an input image for 
each part for checking the property of the input image. Two image quality 
degradation items of edge business and blur are provided. An example will 
be discussed wherein easy occurrence (effect degree) of degradation for 
each of the edge business and blur is mapped in one dimension for each of 
an output machine (printer), an image coding system (quantization matrix), 
and an input image property. The edge business refers to jaggy distortion 
resulting from widening or narrowing the edge width. The blur refers to 
distortion viewed as a blur resulting from suppressing high-frequency 
signals. 
The ninth embodiment is characterized by the fact that when the printer 
type and a quantization matrix are input for one input block, the image 
quality when the input block is output to the printer is predicted. 
FIG. 22 shows the ninth embodiment as a whole. In the figure, numeral 251 
is an input image, numeral 252 is a blocking circuit for blocking an input 
image, numeral 253 is a DCT circuit for discrete cosine transforming the 
image blocks, numeral 254 is a quantization circuit for quantizing a DCT 
coefficient, numeral 255 is a coding circuit for assigning a code to the 
quantized DTC coefficient, and numeral 256 is code. Further, numeral 257 
is block image provided by blocking input image, numeral 258 is an input 
image edge business effect degree determination circuit for determining 
the edge business effect degree of the block image 257, numeral 259 is an 
input image blur effect degree determination circuit for determining the 
blur effect degree of the block image 257, numeral 260 is an input image 
edge business effect degree, numeral 261 is an input image blur effect 
degree, numeral 262 is a printer performance input circuit for inputting 
printer performance, numeral 263 is printer performance, numeral 264 is an 
output device edge business effect degree determination circuit for 
determining an output device edge business effect degree, numeral 265 is 
an output device blur effect degree determination circuit for determining 
an output device blur effect degree, numeral 266 is an output device edge 
business effect degree, numeral 267 is an output device blur effect 
degree, numeral 268 is a quantization matrix input circuit for entering a 
quantization matrix in the quantization circuit 254, numeral 269 is a 
quantization matrix, numeral 270 is a coding system edge business effect 
degree determination circuit for determining the effect degree of a coding 
system on edge business, numeral 271 is a coding system blur effect degree 
determination circuit for determining the effect degree of a coding system 
on blur, numeral 272 is a coding system edge business effect degree, 
numeral 273 is a coding system blur effect degree, numeral 274 is a 
quantization matrix, numeral 275 is an edge business degree determination 
circuit for determining the amount of edge business, one of image quality 
degradation items, numeral 276 is a blur degree determination circuit for 
determining the amount of a blur, one of image quality degradation items, 
numeral 277 is an edge business degree, numeral 278 is a blur degree, 
numeral 279 is a total image quality determination circuit for determining 
total image quality from the edge business degree and the blur degree, and 
numeral 280 is total image quality. 
Next, the operation of the ninth embodiment will be discussed. In FIG. 22, 
an input image is coded by the operation similar to JPEG. That is, the 
blocking circuit 252 divides the input image 251 into 8.times.8 blocks and 
the DCT circuit 253 discrete cosine transforms the blocks and outputs a 
DCT coefficient. The quantization circuit 254 quantizes the DCT 
coefficient and the coding circuit 255 assigns a code to the quantization 
result and outputs the code 256. The quantization matrix used with the 
quantization circuit 254 is input through the quantization matrix input 
circuit 268. 
Further, in FIG. 22, the block image 257 is input to the input image edge 
business effect degree determination circuit 258 and the input image blur 
effect degree determination circuit 259. The operation of the input image 
edge business effect degree determination circuit 258 and the input image 
blur effect degree determination circuit 259 will be discussed below. 
The input image edge business effect degree determination circuit 258 
determines how much easily the input block image produces edge business. 
The operation of the input image edge business effect degree determination 
circuit 258 will be discussed with reference to FIG. 23. In the figure, 
numeral 291 is an edge detection circuit, numeral 292 is a binarization 
circuit, and numeral 293 is a line width detection circuit. The edge 
detection circuit 291 determines whether or not the input block 257 
contains an edge. If an edge is contained, the edge detection circuit 291 
enters the input block 257 in the binarization circuit 292; if an edge is 
not contained, the edge detection circuit 291 outputs the edge business 
effect degree 260 as the lowest value. If a signal of the input block 257 
is greater than a predetermined threshold value, the binarization circuit 
292 sets the signal to 1; if a signal of the input block 257 is less than 
the predetermined threshold value, the binarization circuit 292 sets the 
signal to 0. As shown in FIG. 24, the line width detection circuit 292 
detects the minimum width among pixels set to 1 in the longitudinal, 
lateral, and diagonal directions as the line width, and outputs the line 
width as the edge business effect degree 260. 
Likewise, the input image blur effect degree determination circuit 259 also 
detects the line width in the input block 257 and outputs the line width 
as the input image blur effect degree 261. 
Here, the input image edge business effect degree determination circuit 258 
and the input image blur effect degree determination circuit 259 are the 
same in operation, but may not necessarily be the same in operation. 
Further, we continues to discuss the operation in FIG. 22. In the figure, 
printer performance used for image output is input through the printer 
performance input circuit 262. Here, the resolution and the number of gray 
levels of a printer are input. 
The output device edge business effect degree determination circuit 264 
determines how much easily edge business occurs visually for the input 
resolution and the input number of gray levels of the printer, and outputs 
the determination as the output device edge business effect degree 266. 
Subjective evaluation is previously executed on a standard image for a 
plurality of types of printers different in resolution or number of grays 
levels. The subjective evaluation value matching the resolution and the 
number of gray levels is used as the output device edge business effect 
degree 266. For example, an MOS evaluation value can be used for the 
subjective evaluation value. A table, for example, as shown in FIG. 27, is 
provided wherein if the resolution and the number of gray levels are 
determined, the output device edge business effect degree 266 is found. 
Then, the resolution and the number of gray levels of a new printer are 
input, whereby the output device edge business effect degree can be found. 
The operation of the output device edge business effect degree 
determination circuit 264 will be discussed with reference to FIG. 28. In 
the figure, numeral 301 is resolution information of a printer, numeral 
302 is number-of-gray-level information of a printer, numeral 303 is an 
address calculation circuit, numeral 304 is an address, numeral 305 is an 
edge business effect degree memory, numeral 306 is an edge business effect 
degree, and numeral 307 is an edge business effect degree calculation 
circuit. The address calculation circuit 303 calculates and outputs the 
address 304 of the edge business effect degree memory 305 from the 
resolution information of printer 301 and the number-of-gray-level 
information of printer 302 input as the printer performance 263. The edge 
business effect degree memory 305 outputs the edge business effect degree 
306 in accordance with the value of the address 304. The edge business 
effect degree calculation circuit 307 outputs the output device edge 
business effect degree 266 from the value of the edge business effect 
degree 306. If the address calculation circuit 303 can uniquely determine 
the address 304 from the resolution information of printer 301 and the 
number-of-gray-level information of printer 302, the edge business effect 
degree calculation circuit 307 does not calculate the edge business effect 
degree and outputs the edge business effect degree 306 output from the 
edge business effect degree memory 305 intact as the output device edge 
business effect degree 266. If the address calculation circuit 303 cannot 
uniquely determine the address 304, the edge business effect degree 
calculation circuit 307 outputs the interpolation and calculation result 
of the values of the edge business effect degrees 306 output from the edge 
business effect degree memory 305 in accordance with the addresses 304 as 
the output device edge business effect degree 266. 
Referring again to FIG. 22, likewise, the output device blur effect degree 
determination circuit 265 determines how much easily a blur occurs 
visually for the input resolution and the input number of gray levels of 
the printer, and outputs the determination as the output device blur 
effect degree 267. Subjective evaluation is previously executed on a 
standard image for a plurality of types of printers different in 
resolution or number of gray levels. The subjective evaluation value 
matching the resolution and the number of gray levels is used as the 
output device blur effect degree. For example, an MOS evaluation value can 
be used for the subjective evaluation value. 
Further, the quantization matrix input circuit 268 enters the quantization 
matrix 274 in the quantization circuit 254 and the coding system edge 
business effect degree determination circuit. 
The coding system edge business effect degree determination circuit 270 
determines how much easily edge business occurs visually for the input 
quantization matrix, and outputs the determination as the coding system 
edge business effect degree 272. Subjective evaluation is previously 
executed on a standard image and a standard print with various 
quantization matrixes. The subjective evaluation value is used as the 
coding system edge business effect degree. For example, an MOS evaluation 
value can be used for the subjective evaluation value. 
The operation of the coding system edge business effect degree 
determination circuit 270 will be discussed with reference to FIG. 29. In 
the figure, numeral 311 is an address calculation circuit, numeral 312 is 
an address, numeral 313 is an edge business effect degree memory, numeral 
314 is an edge business effect degree, and numeral 315 is an edge business 
effect degree calculation circuit. The address calculation circuit 311 
calculates and outputs the address 312 of the edge business effect degree 
memory 313 from the quantization matrix 269. The edge business effect 
degree memory 313 outputs the edge business effect degree 314 in 
accordance with the value of the address 312. The edge business effect 
degree calculation circuit 315 outputs the coding system edge business 
effect degree 272 from the value of the edge business effect degree 314. 
If the address calculation circuit 311 can uniquely determine the address 
312 from the quantization matrix 269, the edge business effect degree 
calculation circuit 315 does not calculate the edge business effect degree 
and outputs the edge business effect degree 314 output from the edge 
business effect degree memory 313 intact as the coding system edge 
business effect degree 272. If the address calculation circuit 311 cannot 
uniquely determine the address 312, the edge business effect degree 
calculation circuit 315 outputs the interpolation and calculation result 
of the values of the edge business effect degrees 314 output from the edge 
business effect degree memory 313 in accordance with the addresses 312 as 
the coding system edge business effect degree 272. 
Referring again to FIG. 22, the coding system blur effect degree 
determination circuit 271 determines how much easily a blur occurs 
visually for the input quantization matrix, and outputs the determination 
as the coding system blur effect degree 273. Subjective evaluation is 
previously executed on a standard image and a standard printer with 
various quantization matrixes. The subjective evaluation value is used as 
the coding system blur effect degree. For example, an MOS evaluation value 
can be used for the subjective evaluation value. 
Next, the input image edge business effect degree 260, the output device 
edge business effect degree 266, and the coding system edge business 
effect degree 272 for the input block 257 are input to the edge business 
degree determination circuit 275. 
The edge business degree determination circuit 275 will be discussed with 
reference to FIG. 25. As shown here, spatial points consisting of three 
dimensions of the input image edge business effect degree, the output 
device edge business effect degree, and the coding system edge business 
effect degree are specified from the input image edge business effect 
degree 260, the output device edge business effect degree 266, and the 
coding system edge business effect degree 272. The points address a memory 
storing the edge business degrees. The edge business degrees at the points 
are previously found from a subjective evaluation experiment and are 
stored at the addresses of the memory. By referencing the memory, the 
subjective evaluation value of edge business when the input block 257 is 
coded with a specified quantization matrix and is output on a specified 
printer is predicted and is output as the edge business degree 277. If the 
edge business degree at the point is not previously found by experiment, 
it can be found by linear interpolation of near edge business degrees. 
FIG. 26 shows and Table 1 lists the experiment results of actual 
measurement of the image quality in item-by-item image quality space. 
TABLE 1 
__________________________________________________________________________ 
Edge business image quality space experiment results 
Line width 1 Line width 2 
Line width 3 
System System 
System 
System 
System 
System 
System 
System 
System 
1 2 3 1 2 3 1 2 3 
__________________________________________________________________________ 
Printer 
5.000 
4.143 
1.286 
4.857 
4.286 
2.286 
4.714 
4.714 
2.857 
Printer 
4.857 
2.857 
1.143 
5.000 
3.429 
1.857 
5.000 
3.857 
2.143 
2 
__________________________________________________________________________ 
In FIG. 26, two types of printers (printers 1 and 2) are used as standard 
printers for measuring image quality. Three types of quantization matrixes 
(systems 1, 2, and 3) are used as quantization matrixes. Further, three 
types of input image line widths (line widths 1, 2, and 3) are used as 
input image edge business effect degrees. The edge business degrees of 18 
lattice points of 2.times.3.times.3 in total that can be specified with 
the three axes are found by a subjective evaluation experiment. Table 1 
lists the measurement results of the image quality at the lattice points. 
To print out on any other printer than the printer 1 or 2, the same method 
as mapping the printers 1 and 2 on the graph of FIG. 26 is used for 
mapping on output device edge business effect degree axes of item-by-item 
image quality space. 
The input image line widths are also mapped on input image edge business 
effect degree axes. Further, to code with a new quantization matrix, 
mapping is executed on image coding edge business effect degree axes on a 
similar criterion to that for mapping SF1, SF2, and SF3. 
For example, Table 1 is entered in a ROM or RAM as a look-up table and 
lattice points are related to ROM or RAM addresses, whereby the table can 
be referenced later. 
FIG. 30 is a block diagram of the edge business degree determination 
circuit. In the figure, numeral 321 is an address calculation circuit, 
numeral 322 is an address, and numeral 323 is an image quality memory. The 
image quality contents in Table 1 are previously stored in the image 
quality memory 323. Assume that the printer number given as an output 
device edge business effect degree is P, that the line width given as an 
input image edge business effect degree is L, and that the quantization 
matrix number given as a coding system edge business effect degree is Q. 
At this time, the address for each number of the image quality memory 323 
can be defined as P.times.9+L.times.3+Q, for example. The image quality 
amounts in Table 1 are stored at the addresses. 
In FIG. 30, the input image edge business effect degree 260, the output 
device edge business effect degree 266, and the coding system edge 
business effect degree 272 are input to the address calculation circuit 
321, which then calculates the address 322 according to the same 
calculation technique as when stored in the image quality memory 323 and 
sends the address 322 to the image quality memory 323, which then outputs 
the memory space contents addressed by the address 322 as the edge 
business degree 277. 
Thus, points in the item-by-item evaluation space of edge business for new 
printers, new input images, and new quantization matrixes can be found. 
Since the image quality is already found at the 18 points listed above, if 
the image quality at a new point is not found, the linear sum of the image 
qualities at several near points already found is used as the edge 
business degree for the new printer, the new input image, and the new 
quantization matrix. 
For example, assume that the edge business effect degree of printer 1 is 
A1, that the edge business effect degree of printer 2 is A2, that the edge 
business effect degree of line width 1 is B1, the edge business effect 
degree of line width 2 is B2, that the edge business effect degree of line 
width 3 is B3, that the edge business effect degree of system 1 is C1, 
that the edge business effect degree of system 2 is C2, and that the edge 
business effect degree of system 3 is C3. Table 1 lists the image 
qualities when input, output, and image coding are performed for each edge 
business effect degree. Assume that the measurement result of the edge 
business effect degree of a new printer is (A1+A2)/2 and that the edge 
business effect degree of new output is B1. To code with the system 1, it 
is predicted that the image quality is an intermediate value of the 
element of line width 1, system 1, printer 1 (5.000) and the element of 
line width 1, system 1, printer 2 (4.857) in Table 1. Then, assume that 
the measurement result of the edge business effect degree of the new 
printer is (A1+A2)/2 and that the edge business effect degree of new 
output is B1. To code with the system 1, it can be predicted that the 
image quality is (5.000+4.857)/2=4.9285. 
If the image quality at a new point is not found, the image quality at the 
nearest point already found is used as the edge business degree for new 
printer, new input image, and new quantization matrix. In this case, the 
address calculation circuit 321 in FIG. 30 finds the nearest lattice 
point. 
Likewise, the blur degree in item-by-item image quality space of blur can 
be found by subjective evaluation. Table 2 lists the experiment results of 
image quality measurement in the item-by-item image quality space for 
blur. The blur degree determination circuit 276 operates based on Table 2 
and outputs the blur degree 278. 
TABLE 2 
__________________________________________________________________________ 
Blur image quality space experiment results 
Line width 1 Line width 2 
Line width 3 
System System 
System 
System 
System 
System 
System 
System 
System 
1 2 3 1 2 3 1 2 3 
__________________________________________________________________________ 
Printer 
4.714 
4.143 
2.000 
4.714 
4.714 
2.857 
4.714 
4.714 
3.714 
Printer 
4.714 
3.286 
1.857 
4.714 
3.714 
2.571 
4.714 
4.143 
2.571 
2 
__________________________________________________________________________ 
Then, the edge business degree 277 and the blur degree 278 are sent to the 
total image quality determination circuit 279, which then outputs the 
total image quality 280. 
The total image quality determination circuit 279 determines the edge 
business degree or blur degree, whichever is the larger in degradation, to 
be the total image quality. 
In the embodiment, subjective evaluation when the effect degrees for the 
image input means or input image portion, the image coding system and 
coding parameter, and the image output means are determined is previously 
determined for each image quality degradation item. Then, the effect 
degree of each image quality degradation item is measured for the image 
input means or input image portion, the effect degree of each image 
quality degradation item is measured for the image coding system and 
coding parameter, and the effect degree of each image quality degradation 
item is measured for the image output means, whereby the predication value 
of the subjective evaluation of each image quality degradation item can be 
found. 
Further, the whole image quality evaluation value can be found from the 
predication value of the subjective evaluation of each image quality 
degradation item. 
Thus, subjective evaluation image quality can be predicted for a 
combination of an image input device or input image, an image coding 
system, and an image output device and the image quality of a coded image 
can be guaranteed. 
Embodiment 10 
In the ninth embodiment, the total image quality determination circuit 279 
determines the edge business degree or blur degree, whichever is the 
larger in degradation, to be the total image quality, but the invention is 
not limited to it. In a tenth embodiment of the invention, total image 
quality S is found as 
EQU S=aA+bB [Expression 1] 
where A is the edge business degree and B is the blur degree. The 
coefficients a and b are previously defined. The tenth embodiment is the 
same as the ninth embodiment in other components and therefore will not be 
discussed again. 
Embodiment 11 
Next, an eleventh embodiment of the invention will be discussed. 
In FIG. 31, numeral 331 is an image input device state input circuit and 
numeral 332 is an image input device state. 
FIGS. 31 and 22 differ in that the input image edge business effect degree 
260 and the input image blur effect degree 261 are found from an input 
block in FIG. 22, whereas an input image edge business effect degree 260 
and an input image blur effect degree 261 are found from the image input 
device state output by the image input device state input circuit 331 in 
FIG. 31. 
The operation of an input image edge business effect degree determination 
circuit 258 in FIG. 31 will be discussed with reference to FIG. 32. In 
FIG. 32, numeral 341 is an address calculation circuit, numeral 342 is an 
address, numeral 343 is an edge business effect degree memory, numeral 344 
is an edge business effect degree, and numeral 345 is an edge business 
effect degree calculation circuit. The address calculation circuit 341 
receives the image input device state 332 and calculates and outputs the 
address 342 of the edge business effect degree memory 343. The edge 
business effect degree memory 343 outputs the edge business effect degree 
344 in accordance with the value of the address 342. The edge business 
effect degree calculation circuit 345 outputs the input image edge 
business effect degree 260 from the value of the edge business effect 
degree 344. If the address calculation circuit 341 can uniquely determine 
the address 342 from the image input device state 332, the edge business 
effect degree calculation circuit 345 does not calculate the edge business 
effect degree and outputs the edge business effect degree 344 output from 
the edge business effect degree memory 343 intact as the input image edge 
business effect degree 260. If the address calculation circuit 341 cannot 
uniquely determine the address 342, the edge business effect degree 
calculation circuit 345 outputs the interpolation and calculation result 
of the values of the edge business effect degrees 344 output from the edge 
business effect degree memory 343 in accordance with the addresses 342 as 
the input image edge business effect degree 260. 
Embodiment 12 
Next, a twelfth embodiment of the invention will be discussed. The ninth to 
eleventh embodiments are characterized by the fact that when the printer 
type and a quantization matrix are input for one input block, the image 
quality when the input block is printed out on the printer is predicted. 
In contrast, the twelfth embodiment is characterized by the fact that 
predicted image quality is fed back into a quantization matrix input 
circuit for providing desired image quality. 
FIG. 33 shows the configuration of the twelfth embodiment. In the figure, 
numeral 351 is a quantization matrix generation circuit and numeral 352 is 
a quantization matrix. 
When an input block 257 is coded, first the quantization matrix generation 
circuit 351 generates a first quantization matrix and sends the 
quantization matrix to a quantization matrix input circuit 268. Total 
image quality 280 predicted with the quantization matrix is input to the 
quantization matrix generation circuit 351. If the image quality is more 
than desired image quality, the quantization matrix generation circuit 351 
controls the quantization matrix so as to lower the image quality; if the 
image quality is less than desired image quality, the quantization matrix 
generation circuit 351 controls the quantization matrix so as to raise the 
image quality. 
Thus, an output image of desired image quality can be provided. 
Embodiment 13 
Next, a thirteenth embodiment of the invention will be discussed. The 
embodiments we have discussed are provided for simply finding image 
quality or for controlling image quality by feedback; the thirteenth 
embodiment is provided for controlling image quality by feedforward. 
In the thirteenth embodiment, a JPEG quantization matrix is broken down 
into a basic quantization matrix and a scaling factor. Assume that the 
quantization matrix can be represented by the product of the basic 
quantization matrix and the scaling factor. 
FIG. 34 shows the configuration of the thirteenth embodiment. In the 
figure, numeral 371 is a basic quantization matrix input circuit, numeral 
361 is an edge business control method determination circuit, numeral 362 
is a blur control method determination circuit, numeral 363 is an edge 
business control method, numeral 364 is a blur control method, numeral 365 
is a scaling factor determination circuit, numeral 366 is a scaling 
factor, numerals 367 and 372 are basic quantization matrixes, and numeral 
369 is a quantization matrix. 
In FIG. 34, the basic quantization matrix input circuit 371 enters the 
basic quantization matrix 372 in a coding system edge business effect 
degree determination circuit 270 and a coding system blur effect degree 
determination circuit 271. 
The operation of the edge business control method determination circuit 361 
will be discussed with reference to FIG. 35. As shown here, spatial points 
consisting of three dimensions of the input image edge business effect 
degree, the output device edge business effect degree, and the coding 
system edge business effect degree are specified from an input image edge 
business effect degree 260, an output device edge business effect degree 
266, and a coding system edge business effect degree 272. The points 
address a memory storing the relationship between the edge business 
degrees and scaling factors. The relationship between the edge business 
degree and scaling factor is as shown in FIG. 36. The relationship between 
the edge business degree and scaling factor at each point is previously 
found from a subjective evaluation experiment and is stored at the address 
of the memory. By referencing the memory, the edge business control method 
363 of the relationship between the subjective evaluation amount of one 
edge business and scaling factor can be found when the input block 257 is 
output on a specified printer with a specified basic quantization matrix. 
If the relationship between the subjective evaluation amount of edge 
business and the scaling factor at the corresponding point is not 
previously found, the relationship between the subjective evaluation 
amount of edge business and the scaling factor at the nearest point 
already found is used. 
FIG. 37 shows and Tables 3 and 4 list the experiment results of actual 
measurement of the image quality in item-by-item image quality space. 
TABLE 3 
______________________________________ 
Experiment results with basic quantization matrix 1 
Line width 1 Line width 2 Line width 3 
SF1 SF2 SF3 SF1 SF2 SF3 SF1 SF2 SF3 
______________________________________ 
Printer 
5.000 4.143 1.286 
4.857 
4.286 
2.286 
4.714 
4.714 
2.857 
Printer 
4.857 2.857 1.143 
5.000 
3.429 
1.857 
5.000 
3.857 
2.143 
2 
______________________________________ 
TABLE 4 
______________________________________ 
Experiment results with basic quantization matrix 2 
Line width 1 Line width 2 Line width 3 
SF1 SF2 SF3 SF1 SF2 SF3 SF1 SF2 SF3 
______________________________________ 
Printer 
4.857 3.857 1.000 
4.857 
4.286 
1.143 
5.000 
4.571 
1.429 
Printer 
4.571 3.857 1.000 
4.714 
4.143 
1.000 
5.000 
4.429 
1.286 
2 
______________________________________ 
In FIG. 37, two types of printers (printers 1 and 2) are used as standard 
printers for measuring image quality. Two types of quantization matrixes 
(basic quantization matrixes 1 and 2) are used as basic quantization 
matrixes. Further, three types of input image line widths (line widths 1, 
2, and 3) are used as input image edge business effect degrees. The 
relationships between the edge business degrees and scaling factors (SF1, 
SF2, and SF3) at 12 lattice points of 2.times.2.times.3 in total that can 
be specified with the three axes are found by a subjective evaluation 
experiment. Table 3 lists the results with the basic quantization matrix 1 
and Table 4 lists the results with the basic quantization matrix 2. 
To print out on any other printer than the printer 1 or 2, the same method 
as mapping the printers 1 and 2 on the graph of FIG. 37 is used for 
mapping on output device edge business effect degree axes of item-by-item 
image quality space. 
The input image line widths are also mapped on input image edge business 
effect degree axes. Further, to code with a new quantization matrix, 
mapping is also executed on image coding edge business effect degree axes 
on a similar criterion to that for mapping the basic quantization matrixes 
1 and 2. 
Thus, points in the item-by-item evaluation space of edge business for new 
printers, new input images, and new quantization matrixes can be found. 
Since the relationship between the image quality and scaling factors is 
already found at the 12 points listed above, the relationship between the 
edge business degree and scaling factor at the nearest point among the 
already found points is uses as the relationship between the edge business 
degree and scaling factor for the new printer, the new input image, and 
the new basic quantization matrix. 
For example, assume that the edge business effect degree of printer 1 is 
A1, that the edge business effect degree of printer 2 is A2, that the edge 
business effect degree of line width 1 is B1, the edge business effect 
degree of line width 2 is B2, that the edge business effect degree of line 
width 3 is B3, that the edge business effect degree of basic quantization 
matrix 1 is C1, and that the edge business effect degree of basic 
quantization matrix 2 is C2. Table 3 lists the image qualities when input, 
output, and image coding are performed for each edge business effect 
degree. Assume that the measurement result of the edge business effect 
degree of a new printer is A1, that the edge business effect degree of new 
output is B1, and that the edge business effect degree of a new 
quantization matrix is C1. In this case, it is predicted that the 
relationship between the scaling factor and the image quality is indicated 
by three elements including line width 1 and printer 1 in Table 3. The 
three elements represent the scaling factor and edge business degree as 
shown in FIG. 36. Here, an example wherein the number of points is three 
is given; if the number of points is increased, more accurate edge 
business degree prediction is enabled. If the desired edge business degree 
is 4.143, SF2 may be selected for coding. 
Likewise, the relationship between the blur degree in item-by-item image 
quality space of blur and scaling factor can be found by subjective 
evaluation. The blur control method determination circuit 362 can also 
find the blur control method 364. 
The scaling factor determination circuit 365 finds a scaling factor for 
setting a predetermined edge business amount and a scaling factor for 
setting a predetermined blur amount, determines the scaling factor for 
making the image quality better between the two scaling factors, and 
outputs the scaling factor 366. 
A multiplier 368 multiplies the basic quantization matrix 367 by the 
scaling factor 366 to generate the quantization matrix 369 and sends the 
quantization matrix 369 to the quantization circuit 254, which then 
executes quantization with the quantization matrix 369 sent from the 
multiplier 368. 
In the thirteenth embodiment, the relationship between coding parameter and 
subjective evaluation value when the effect degrees for the image input 
means or input image portion, the image coding system, and the image 
output means are determined is previously determined for each image 
quality degradation item. Then, the effect degree of each image quality 
degradation item is measured for the image input means or input image 
portion, the effect degree of each image quality degradation item is 
measured for the image coding system, and the effect degree of each image 
quality degradation item is measured for the image output means, whereby 
the relationship between the coding parameter and the predication value of 
the subjective evaluation of each image quality degradation item can be 
found. 
Further, a coding parameter satisfying predetermined subjective evaluation 
image quality can be found from the relationship between the coding 
parameter and the predication value of the subjective evaluation of each 
image quality degradation item. 
Embodiment 14 
Next, a fourteenth embodiment of the invention will be discussed. In the 
first to thirteenth embodiments, the image quality degradation items are 
edge business and blur, but not limited to them. The following image 
quality degradation items and the following input image effect degree 
determination techniques and output device effect degree determination 
techniques related to the image quality degradation items are available. 
(1) Pseudo Contour 
Input image effect degree determination technique: Prepares a frequency 
distribution of pixel values of an input image and measures the number of 
pixel value types. 
Output device effect degree determination technique: Measures the effective 
number of gray levels of printer. 
(2) Block Distortion 
Input image effect degree determination technique: Measures pixel value 
change of block peripheral pixels. 
Output device effect degree determination technique: Measures the effective 
number of gray levels and frequency characteristic of printer. 
(3) Granular Noise 
Input image effect degree determination technique: Measures power of 
signals at high and low frequencies of input image. 
Output device effect degree determination technique: Measures the effective 
number of gray levels and frequency characteristic of printer. 
(4) Beat 
Input image effect degree determination technique: Measures power of 
signals at high and low frequencies of input image. 
Output device effect degree determination technique: Measures the effective 
number of gray levels and frequency characteristic of printer. 
(5) Mosquito Noise 
Input image effect degree determination technique: Measures power of 
signals at high and low frequencies of input image. 
Output device effect degree determination technique: Measures the effective 
number of gray levels and frequency characteristic of printer. 
Embodiment 15 
Next, a fifteenth embodiment of the invention will be discussed. In the 
embodiments, the input image effect degree, the output device effect 
degree, and the coding system effect degree can be all mapped in a 
one-dimensional space, but the mapping space is not limited to the 
one-dimensional space. 
For example, the number of pixel value types of pixel values 0-127 and the 
number of pixel value types of pixel values 128-255 of an input image are 
taken, whereby the pseudo contour effect degrees of the input image can be 
mapped in a two-dimensional space. 
Embodiment 16 
Next, a sixteenth embodiment of the invention will be discussed. In the 
embodiments, the coding system is JPEG, but not limited to it. 
A prediction coding system can control image quality by changing the 
quantization step size. It is a coding system for using an already coded 
pixel to predict the next pixel value and coding a prediction error 
thereof. 
A prediction error signal distribution becomes a distribution leaning to 0 
as shown in FIG. 38. The quantization step for quantizing the prediction 
error signal is determined in various manners. Nonlinear quantization and 
linear quantization as shown in FIG. 39 are available. The vertical lines 
in FIG. 39 denote quantization threshold values. 
The quantization patterns can be handled as quantization matrixes in JPEG. 
Embodiment 17 
Next, a seventeenth embodiment of the invention will be discussed. 
The seventeenth embodiment is realized by providing the seventh embodiment 
in a more specific configuration. It will be discussed with reference to 
FIGS. 12-17, 22, 23, 28, 29, 31, and 33. The components, reference 
numbers, etc., shown in FIGS. 22, 23, 28, 29, 31, and 33 are the same as 
those described in the corresponding embodiments. The operation in FIGS. 
22, 31, and 33 is also the same as that described in the corresponding 
embodiments. 
Here, the correspondence between the components shown in FIGS. 22, 23, 28, 
29, 31, and 33 and those shown in FIGS. 12-17 will be mainly discussed and 
specific configuration will not be discussed in detail. 
First, the correspondence with the components in FIG. 13 will be discussed. 
In FIGS. 22, 31, and 33, the input image edge business effect degree 
determination circuit 258 and the input image blur effect degree 
determination circuit 259 correspond to the input image property input 
means 152. The output device edge business effect degree determination 
circuit 264 and the output device blur effect degree determination circuit 
265 correspond to the image output property input means 155. The coding 
system edge business effect degree determination circuit 270 and the 
coding system blur effect degree determination circuit 271 correspond to 
the image coding property input means 158. The edge business degree 
determination circuit 275 and the blur degree determination circuit 276 
correspond to the image quality prediction means 160. The edge business 
degree 277 and the blur degree 278 correspond to the item-by-item image 
quality 161. The total image quality determination circuit 279 corresponds 
to the total image quality prediction means 163. Further, the total image 
quality 280 corresponds to the total image quality 164. 
The correspondence with the components in FIG. 15 will be discussed. In 
FIG. 23, the edge detection circuit 291 corresponds to the image analysis 
means 182. The binarization circuit 292 and the line width detection 
circuit 293 correspond to the input image quality effect degree 
calculation means 184. 
The correspondence with the components in FIG. 16 will be discussed. In 
FIG. 28, the address calculation circuit 303 and the edge business effect 
degree memory 305 correspond to the image output image quality effect 
degree storage means 192. The edge business effect degree calculation 
circuit 307 corresponds to the image output image quality effect degree 
calculation means 194. 
The correspondence with the components in FIG. 17 will be discussed. In 
FIG. 29, the address calculation circuit 311 and the edge business effect 
degree memory 313 correspond to the image coding image quality effect 
degree storage means 201. The edge business effect degree calculation 
circuit 315 corresponds to the image coding image quality effect degree 
calculation means 203. 
The operation of the input image edge business effect degree determination 
circuit 258, the input image blur effect degree determination circuit 259, 
the output device edge business effect degree determination circuit 264, 
the output device blur effect degree determination circuit 265, the coding 
system edge business effect degree determination circuit 270, the coding 
system blur effect degree determination circuit 271, the edge business 
degree determination circuit 275, the blur degree determination circuit 
276, and the total image quality determination circuit 279 is the same as 
that described in the ninth embodiment. 
It is clear that likewise, the eighth embodiment can also be realized 
specifically. 
Embodiment 18 
Next, an eighteenth embodiment of the invention will be discussed. 
The eighteenth embodiment will be discussed with reference to FIGS. 18-20 
and 34. The components, reference numbers, etc., shown in FIG. 34 are the 
same as those described in the thirteenth embodiment. The operation in 
FIG. 34 is the same as that described in the ninth embodiment. 
Here, the correspondence between the components shown in FIG. 34 and those 
shown in FIGS. 18-20 will be mainly discussed. 
In FIG. 34, the input image edge business effect degree determination 
circuit 258 and the input image blur effect degree determination circuit 
259 correspond to the input image property input means 152. The output 
device edge business effect degree determination circuit 264 and the 
output device blur effect degree determination circuit 265 correspond to 
the image output property input means 155. The coding system edge business 
effect degree determination circuit 270 and the coding system blur effect 
degree determination circuit 271 correspond to the image coding property 
input means 158. The edge business control method determination circuit 
361 and the blur control method determination circuit 362 correspond to 
the image quality control method determination means 220. The edge 
business control method 363 and the blur control method 364 correspond to 
the item-by-item image quality control method 221. The scaling factor 
determination circuit 365 corresponds to the total image quality control 
means 223. Further, the scaling factor 366 corresponds to the coding 
parameter 224. In the embodiment, desired image quality is fixed and 
desired image quality input means 211 is previously built in the scaling 
factor determination circuit 365. 
The operation of the input image edge business effect degree determination 
circuit 258, the input image blur effect degree determination circuit 259, 
the output device edge business effect degree determination circuit 264, 
the output device blur effect degree determination circuit 265, the coding 
system edge business effect degree determination circuit 270, the coding 
system blur effect degree determination circuit 271, the edge business 
control method determination circuit 361, the blur control method 
determination circuit 362, and the scaling factor determination circuit 
365 is the same as that described in the thirteenth embodiment. 
Further, the configuration wherein the desired image quality input means 
211 is not previously built in the scaling factor determination circuit 
365 will be discussed with reference to FIG. 40. 
In FIG. 40, numeral 381 is a desired image quality input circuit and 
numeral 382 is desired image quality. Other reference numerals are 
identical with those in FIG. 34. 
In FIG. 40, the desired image quality input circuit 381 corresponds to the 
desired image quality input means 211 and the desired image quality 382 
corresponds to the desired image quality 212. 
The scaling factor determination circuit 365 determines a scaling factor 
from the relationship between the edge business degree and scaling factor 
as shown in FIG. 36. The input desired image quality contains a desired 
edge business degree and a scaling factor is determined from the desired 
edge business degree as shown in FIG. 36. 
Likewise, a scaling factor is determined from a desired blur degree. 
Thus, the scaling factor determination circuit 365 determines scaling 
factors from one or more image quality degradation items and outputs the 
smallest scaling factor for producing the best image quality from among 
the determined scaling factors. 
As we have discussed, according to the invention, image quality is measured 
for each divided image quality item, whereby various input physical 
amounts of an input image property, an output machine property, an input 
machine property, an image coding system property, etc., can be mapped in 
a space consisting of continuous parameters. Further, the total image 
quality can be found from the image quality for each image quality item, 
whereby when a plurality of physical amounts are measured, image quality 
control can also be performed.