Method of optimizing the compression of image data, with automatic selection of compression conditions

A method of optimizing the compression of image data in accordance with at least one compression objective includes an initialization phase including the steps of compressing a plurality of test samples in accordance with at least two different compression conditions, associating with each test sample at least one item of information characteristic of the content of the sample and at least one measurement of the corresponding compression results and establishing at least one reference function associating at least one of the compression objectives and/or at least one compression result measurement and/or at least one of the items of characteristic information for each of the compression conditions. The method additionally includes a selection phase including for each set of image data to be compressed the steps of determining at least one item of information characteristic of the set of image data to be compressed and selecting the optimal compression condition that maximizes at least one of the compression objectives in accordance with the item or items of characteristic information and the reference functions.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The field of the invention is that of compressing fixed images and moving 
images. To be more precise, the invention concerns optimizing compression 
in accordance with one or more criteria chosen by the user. 
2. Description of the Prior Art 
Image compression is necessary in a very large number of fields, in 
particular with the aim of obtaining small files and/or files that can be 
transmitted quickly. 
The fast transmission of images, in particular, is becoming essential in 
many fields of application and the image will assume an ever more dominant 
place in telecommunications. 
All multimedia applications, in particular, require fast transmission of 
images and high-performance image storage techniques. This imperative is 
encountered, for example, in fields concerning videophones, 
video-conferencing, electronic mail, telediagnosis, interactive television 
(VOD: "Video On Demand" and IVOD: "Interactive VOD") up to televirtual 
reality. The same applies to remote sensing satellites: the increasing 
resolution of the instruments means that they have to transmit ever 
greater quantities of image data. 
In all cases new image compression techniques are necessary for improved 
image storage and transmission, depending on the bit rates available, 
depending on the transmission speed required given the transmission time 
required and the degree to which information is lost on transmission that 
can be tolerated. 
The standard compression techniques developed over the last five years 
(JPEG, MPEG) are mainly based on DCT (Discrete Cosine Transform). The 
compression ratio is generally less than 20 for fixed images and less than 
80 for moving images. The increasing importance of the image in 
telecommunications requires the use of ever more intelligent compression 
techniques offering ever higher performance. Image compression is 
therefore one of the crucial features of future telecommunication tools. 
The invention concerns such developments and applies in particular to 
development of the standard JPEG and MPEG techniques mentioned above. 
There are many compression algorithms. They can be classified into a number 
of major families, respectively based on DCT, wavelet transformation, IFS 
fractals, vector quantification and quad-trees. 
Each of these algorithms has its own advantages and disadvantages, 
depending on the type of image concerned, the required compression ratio 
and/or the required quality of image reconstruction. On the other hand, 
none of them can guarantee optimal results under all conditions. 
The ideal would therefore be to be able to use all of these algorithms and 
to choose the most effective algorithm, depending on stated objectives, in 
each individual case. 
However, an a priori knowledge of the most effective algorithm for given 
conditions and a given image is virtually impossible for a user, 
especially one who is not an image processing specialist. The problem is 
made worse by the fact that most of these algorithms necessitate 
adjustment of one or more parameters. 
It is no more feasible to try all possible configurations (choice of 
algorithm+choice of parameters) for each image to be transmitted or 
stored. 
Prior art compression systems therefore use a single type of compression 
algorithm. They nevertheless remain somewhat difficult to use because it 
is necessary to define one or more parameters. 
What is more, the various systems operate on complete images but the 
inventors have found that in many situations only part of the image is of 
major interest. 
One objective of the invention is to alleviate the various drawbacks of the 
prior art. 
To be more precise, one objective of the invention is to provide a method 
of optimizing the compression of images and/or sequences of images capable 
of optimizing one or more criteria, or compression objectives, without 
necessitating from a user any analysis or any a priori choice. 
In other words, the objective of the invention is to provide a method of 
the above kind that is accessible to any type of user, and in particular 
to users who are not image processing specialists, and which guarantees 
optimized compression regardless of the type of image and in accordance 
with objectives set by the user. 
Another objective of the invention is to provide a method of the above kind 
that can assure optimized compression of a very large number of types of 
images, whatever the objectives set by the user (image quality or 
compression ratio). 
SUMMARY OF THE INVENTION 
The above objectives, and others that will emerge hereinafter, are achieved 
in accordance with the invention by a method of optimizing the compression 
of image data in accordance with at least one compression objective which 
includes: 
an initialization phase including the steps of: 
compressing a plurality of test samples in accordance with at least two 
different compression conditions, 
associating with each of the test samples at least one item of information 
characteristic of the content of the sample and at least one measurement 
of the corresponding compression results, and 
establishing at least one reference function associating at least one of 
the compression objectives and/or at least one compression result 
measurement and/or at least one of the items of characteristic information 
for each of the compression conditions, and 
a selection phase including for each set of image data to be compressed the 
steps of: 
determining at least one item of information characteristic of the set of 
image data to be compressed, and 
selecting the optimal compression condition that maximizes at least one of 
the compression objectives in accordance with the item or items of 
characteristic information and the reference functions. 
It is therefore possible to obtain adaptive compression, the optimal 
compression conditions being chosen automatically, without the user having 
to make any choices or to set any parameters, apart from indicating an 
objective for the compression to be effected. The method makes the choices 
itself, with reference to functions based on tests carried out during 
initialization. 
A compression condition advantageously corresponds to a particular 
compression algorithm and/or to particular parameter values of a 
compression algorithm. 
The compression algorithms can be selected from the group comprising: 
algorithms based on the use of a frequency transform such as DCT, 
algorithms based on the use of a wavelet transform, 
algorithms based on the use of fractals or multi-fractals, 
algorithms based on the use of vector quantification, and 
algorithms based on the use of quad-trees. 
The setting of parameters can involve at least one of the parameters 
selected from the group comprising: 
a target compression ratio, 
a reconstruction error or a quality parameter or an error tolerance, 
a minimal number and/or a maximal number of subdivisions of a set of image 
data, and 
parameters intrinsic to a given compression algorithm. 
The compression objectives are advantageously selected from the group 
comprising: 
a reconstruction quality of the set of image data, 
an RMS error, 
a percentage of quality conserved, 
a compression ratio, 
a compression and/or decompression time, 
a transmission time, 
a compressed file size, and 
a symmetry of compression. 
The compression result measurements can be selected from the group 
comprising: 
a measurement of the reconstruction quality of the set of image data, and 
the compression ratio. 
The measurement of reconstruction quality is, for example, selected from 
the group comprising: 
a measurement of the RMS error between the set of source image data and the 
set of decompressed image data, 
a measurement of the maximum reconstruction error, 
a measurement of the signal to noise ratio, 
a measurement of the peak signal to noise ratio, and 
a psycho-visual quality measurement. 
The characteristic information can be selected from the group comprising: 
a measurement of the entropy of the set of image data, 
a measurement of the complexity of the set of image data, 
a measurement of the frequency power of the set of image data, and 
a measurement of the texturing of the set of image data. 
In one particular embodiment of the invention the measurement of the 
complexity is a measurement on one dimension conforming to the formula: 
##EQU1## 
In another particular embodiment of the invention the measurement of the 
complexity is a measurement on two dimensions conforming to the formula: 
##EQU2## 
The reference functions are preferably selected from the group comprising 
the functions of the following types: 
quality measurement(s)=f(compression objective(s) and/or compression result 
measurement(s) and/or image characteristic(s)), compression 
objective(s)=f(compression condition(s) and/or image characteristic(s) 
and/or quality measurement(s)). 
The reference functions can be expressed in at least one of the forms 
selected from the group comprising: 
three-dimensional graphs, 
graphs, 
tables of values, 
mathematical functions, and 
decision rules. 
The reference functions are advantageously obtained by the following steps: 
analyzing compression results for each test sample and for each compression 
condition, and 
smoothing the analysis results. 
The test samples and/or the image data can be of any type, for example: 
images, 
portions or zones of images, 
series of similar images, and 
frequencies of moving images. 
The selection step preferably also allows for at least one of the secondary 
criteria selected from the group comprising: 
the size or the format of the set of image data, 
the brightness of the set of image data. 
According to one advantageous feature of the invention the method includes 
a focusing step for selecting at least one portion in an image and/or at 
least one image in a sequence of images and/or an identical image portion 
in a sequence of images. 
In this case two situations can arise: 
at least one compression objective is fixed for the image portion or 
portions and/or at least one image in a sequence of images and/or an 
identical image portion in a sequence of images and at least one 
compression objective is fixed for the remainder of the image data; or 
at least one compression objective is fixed for the image portion or 
portions and/or at least one image in a sequence of images and/or an 
identical image portion in a sequence of images and for said set of image 
data. 
The method of the invention can also include a step of continuous 
visualization of a set of image data being decompressed. A focusing step 
can then be effected on a set of image data being decompressed. 
The plurality of test samples is advantageously chosen in accordance with 
one or more types of sets of image data and/or can be added to at any 
time. 
According to another feature of the invention, making it simpler to use, 
the initialization phase includes a parameter definition step enabling 
uniform parameter values to be set, thereby associating the same 
reconstruction quality and compression ratio parameters with all 
compression conditions. 
The selection phase preferably includes a conflict management step in the 
event of incompatibility between the compression objectives and/or in the 
context of implementation of the focusing step. 
In one advantageous embodiment of the invention the method is in the form 
of a software toolbox including the following tools: 
a selection tool implementing the selection step, 
at least two compression algorithms, and 
a focusing tool implementing the focusing step. 
Other features and advantages of the invention will become more clearly 
apparent upon reading the following description of a preferred embodiment 
of the invention given by way of illustrative and non-limiting example 
only and from the appended drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The embodiment described below by way of example concerns the compression 
of fixed images. It will be a simple matter for the skilled person to 
adapt this example to the compression of moving images or to particular 
types of images (satellite, medical, black and white, . . . ). 
As already indicated, the main aim of the invention is to satisfy 
automatically the performance objective of a user of an image compression 
platform, whatever the nature of the image. 
FIG. 1 shows the general approach of the method of the invention. The 
method of the invention, or "adaptive mode", associates one of the 
available compression algorithms 11 and parameters 12 for that algorithm 
with each image 13, or to be more precise with the features of that image, 
in accordance with requirements 14 stated by the user. 
The general principle of the method of the invention is described first. 
Detailed examples of various aspects are then described. 
The optimization method of the invention is in two separate phases: an 
initialization phase (illustrated in FIG. 2) and a selection phase 
(illustrated in FIG. 3). 
The aim of the initialization phase is to define functions for associating 
with a given image, characterized by at least one item of characteristic 
information (for example complexity) and at least one compression 
objective (for example compression ratio or quality level), the best 
compression conditions, that is to say the most efficient algorithm and/or 
the best parameters for that algorithm, allowing for the objective of the 
user. 
The selection functions can constitute associations between the various 
following parameters: compression objectives, information characteristic 
of the image (or on the image portion or image sequence) and compression 
conditions. They can be in form of three-dimensional graphs as shown in 
FIGS. 4 through 6, tables, mathematical functions and/or decision rules. 
The initialization phase is illustrated in FIG. 2. A series of test images 
21 is used (of course, references to images hereinafter are to be 
understood as encompassing portions of images and sequences of images and 
in particular moving images). 
The test images are naturally chosen to cover the greatest possible 
diversity of images (tailored if necessary to a particular application). 
With each image there is associated (step 22) one or more items of 
characteristic information such as a complexity indicator. Each of the 
test images is compressed in parallel (step 23) using all of the available 
algorithms, and if appropriate with a number of parameter configurations. 
Finally, the compression results (compression ratio and/or image 
reconstruction quality) are analyzed (step 24) to produce reference 
functions 25 used to select the optimal compression conditions in the 
selection phase. 
The selection phase is illustrated in FIG. 3. With each image 31 to be 
compressed there is associated one or more items of characteristic 
information, in a similar fashion to step 22 already discussed. 
Then, according to the objective 33 fixed by the user or a predetermined 
objective, characteristics 34 of the image and reference functions 25, 
compression conditions are selected (step 35) which designate a 
compression algorithm 36 and, where applicable, parameters 37 for that 
algorithm. 
The image is then compressed (step 38) under optimal conditions without 
users having to fix the compression conditions themselves. 
The tools used (section 1), the initialization phase (section 2) and the 
selection phase (section 3) of the preferred embodiment will now be 
described in more detail, together with various complementary aspects 
(section 4). 
1. Tools Used 
1.1 Compression Algorithms 
As already indicated, there are several large families of algorithms, 
respectively based on DCT, wavelet transforms, IFS fractals, vector 
quantification and quad-trees. 
In the embodiment described below an algorithm based on DCT (called JPEG 
hereinafter), an algorithm based on wavelets (called EZW or SHAPIRO 
hereinafter) and an algorithm based on fractals (called FISHER 
hereinafter) are used. 
1.1.1 JPEG Algorithm 
JPEG (Joint Photograph Expert Group) is the generic name for the standard 
compression algorithms described in ISO/IEC 10918-1. 
The JPEG algorithm used corresponds to the "DCT based sequential mode, 
Huffman baseline" algorithm of the standard, which is a lossy compression 
algorithm. The general principle of the lossy JPEG compression algorithm 
is as follows: 
take the next block of 8.times.8 pixels, calculate the Discrete Cosine 
Transform (DCT) of that block, 
divide each of the 8.times.8 coefficients obtained by a specific value (the 
table giving the value for each frequency is called the quantification 
table) and round off the result to the nearest integer, and 
apply entropic coding (which essentially consists in putting the quantified 
coefficients into a zig-zag order followed by Run Length Encoding (RLE) 
and then either Huffman or arithmetical coding). 
1.1.2 SHAPIRO algorithm (EZW) 
The name SHAPIRO designates another image compression algorithm based on 
wavelet transforms, invented and described by J. M. Shapiro. 
The SHAPIRO algorithm pushes filtering by the wavelet transform to the 
final level (for which there remains only a coefficient equal to the 
result of successive convolution of the image with the low-pass filter, 
which is generally close to the average for the image). However, the 
essential novelty of the SHAPIRO algorithm lies in the organization and 
the coding of the coefficients obtained: 
the coefficients are coded in decreasing magnitude order, that is to say 
the largest coefficients are coded first, independently of the frequency 
to which they correspond, and 
to finish, the sequences of symbols (corresponding to "positive", 
"negative", "negligible branch route" and "isolated zero") are subject to 
"on the fly" entropic coding of the adaptive arithmetical coding type. 
1.1.3 FISHER Algorithm 
The name FISHER designates a fractal image compression algorithm of the 
Iterated Function System (IFS) type described by Y. Fisher. 
The general principle of IFS algorithms is to look for auto-similarities in 
the image to deduce therefrom a transform which on iteration converges 
towards a fixed point as close as possible to the original image. 
In more concrete terms the steps of IFS compression are as follows: 
choose a "non-overlapping" partition of the image (a set of regions in 
which all the two by two intersections are empty, but the union of which 
constitutes the entire image), 
for each region of the partition, look in the image for a larger "domain" 
and a contracting transform such that the transform of the domain is as 
similar as possible to the region, and 
code the parameters of the transforms "cleverly" and if applicable apply 
entropic coding to them. 
The size of the IFS compressed data is equal to the number of regions in 
the partition multiplied by the number of bits necessary to code the 
parameters of each pair (domain, transform), and where applicable divided 
by the compression ratio obtained by a final phase of entropic coding. 
1.1.4 Other Algorithms 
Many other algorithms can be used such as the TETRA algorithm based on 
quad-tree image coding, the EPIC (Efficient Pyramid Image Coder) algorithm 
based on wavelet transforms, no-loss algorithms, etc. 
1.2 Reconstruction Quality 
Many criteria can be envisaged for measuring the quality of reconstruction, 
possibly used in combination: 
1.2.1 RMS Error 
The RMS Error between the source image and the reconstructed image is a 
statistical measurement of error given by the following formula: 
##EQU3## 
This error translates the error power between the source and decompressed 
images. Minimizing the RMS error is a basic criteria of many compression 
algorithms. However, the RMS error can be a poor indication of the 
subjective quality of reconstruction, not allowing for the reaction of the 
eye to the visibility of defects. This being said, in general an image 
with a high RMS error shows more distortion than one with a lower RMS 
error. 
Moreover, the RMS error has a relative digital stability that is of benefit 
in the case of performance comparison, for tracking the evolution of the 
error as a function of other factors. 
1.2.2 Psycho-visual Criteria (ICM) 
IQM is the name of a psycho-visual approach to measuring image distortion. 
The quality of a coding system is ideally measured by the subjective 
quality of the reconstructed image obtained by decoding. Unfortunately 
there is as yet no universal measurement of quality. On the one hand 
quality depends on the application and on the other hand it must allow for 
the still imperfectly defined properties of the human visual receptor. 
Nevertheless, the quality scale used for professional imaging in television 
will be used. This scale is quantified into five levels of visible 
degradation: 
1: imperceptible, 
2: perceptible but not irritating, 
3: slightly irritating, 
4: irritating, and 
5: very irritating. 
The approach is for non-specialists to analyze the original images and then 
the reconstructed images from a psycho-visual point of view. The viewing 
distance varies in the range four times to six times the screen height (4 
H or 6 H). A coding technique is deemed satisfactory for an average score 
of 4.5 at 6 H. 
The following procedure is recommended: the first task is to choose test 
images that are highly specific to the application, in collaboration with 
operational experts. The results are then validated from the subjective 
point of view with non-specialists and then experts. 
Many researchers have attempted to define a psycho-visual measurement 
model. We have chosen IQM as being the most comprehensive and having the 
best correlation with visual impression, based on tests carried out by the 
original authors and confirmed by our own experiments. 
It was initially developed for aerial image applications with the aim of 
qualifying image quality absolutely, to determine whether an image is 
usable or not. We have therefore adapted it to apply it to the 
reconstruction calculation between an initial image and an image 
reconstructed after compression-decompression. 
A function of the above kind provides a measurement that takes better 
account of the perception of degradation by the human eye. However, it 
does not give any relationship between the values of this measurement and 
the above perceptual scale. Establishing this relationship is a remaining 
task for future work on the adaptive mode. This having been said, the 
problem can be circumvented because in most cases the specification of the 
quality criteria in the context of a compression will be effected in the 
context of focusing and will concern the quality of some areas of the 
image relative to others. The relative IQM values from the different areas 
can therefore be used without relating them in any absolute way with the 
perceptual scale previously mentioned. 
1.2.3 Other Criteria 
There are other statistical measurements that can be used, fairly close to 
or derived from RMS error, such as the maximum reconstruction error, the 
signal-to-noise ratio or the peak signal-to-noise ratio. 
1.3. Image Characterization 
1.3.1 1D Complexity 
This parameter is an estimate of complexity used in particular in the 
regulation system of the JPEG-based SPOT5 algorithm. 
The complexity is the mean of the differences between each pixel and its 
immediate lefthand neighbor. Although it is highly unsophisticated, this 
criterion takes good account of local regularity of an image and therefore 
to some degree of its texture. 
The formula for obtaining the complexity of an image is as follows: 
##EQU4## 
1.3.2 2D Complexity 
The complexity criterion mentioned above is the one we have found to be 
most reliable in our preliminary experiments. This reliability is 
reflected in the accuracy of the three-dimensional graphs used for JPEG 
compression. 
As already stated, the criterion is somewhat rudimentary and in particular 
takes account of differences between pixels only in the horizontal 
direction. 
It would therefore be desirable to consider a similar but bidirectional 
criterion, for example: 
##EQU5## 
1.3.3 Entropy 
The entropy of a file, as defined by the following formula, is the number 
of bits needed to represent the file: 
##EQU6## 
Although the number of bits used to store a computer file is systematically 
8 bits per character, in principle this number of bits can be reduced to 
that indicated by the entropy. This is the objective of entropic coding 
such as Huffman coding. 
From a point of view close to the image, a low entropy is characteristic of 
the fact that the pixels take a small number of values in the range 
[0,255]. This is in principle representative of the information contained 
in the image and of the difficulty of compressing it, in that if the 
pixels take a small number of values, variations of intensity should be 
relatively infrequent. 
In practise this is not necessarily so (an image could use only two pixel 
values, present alternately, and such an image would be more difficult to 
code than an image containing more pixel values, concentrated in uniform 
areas). 
1.3.4 Frequency Power 
The frequency power is the mean value of the image power spectrum. The 
power spectrum is itself defined as the modulus of the Fourier transform 
of the image: 
EQU .vertline.F(u,v).vertline.=[R.sup.2 (u,v)+I.sup.2 (u, v)].sup.1/2 
where the Fourier transform of the image 
##EQU7## 
is represented in the modulus/phase form: 
EQU F(u,v)=R(u,v)+jI(u,v) 
1.3.5 Other Criteria 
Equally feasible are the use of: 
criteria using DCT: it can also be beneficial to consider a criterion using 
the frequency representation of the image to evaluate its contents, and a 
criterion of this kind can be constructed using a block DCT that renders 
its values particularly favorable for evaluating the ease of compressing 
the image using JPEG, and 
criteria using wavelet transforms: it can also be beneficial to consider a 
criterion using wavelet transforms, for example by calculating the entropy 
of the wavelet coefficient resulting from the transform of the image. 
A criteria of this kind would be capable a priori of allowing for the ease 
of compressing the image with an algorithm using wavelets. 
2. Initialization Phase 
2.1 Principle 
As indicated above, the invention is based on the use of three-dimensional 
graphs (or rules, functions, tables, . . . ) based on analyzing the 
results of compressing a set of test images. 
2.2 Test Images 
In the example under discussion a base of 50 images was used, divided into 
three categories: 
natural images: images of everyday scenes such as faces, animals, 
landscapes, 
aerial images: aerial views taken under operational conditions with 
different resolutions, including photos of industrial, residential and 
harbor areas, and 
satellite images: SPOT images covering urban, agricultural and mountainous 
areas. 
2.3 Production of Three-dimensional Graphs 
2.3.1 Principle 
Each of the above images is compressed using all the algorithms. Depending 
on the compression results, three-dimensional graphs of the following 
types are generated: 
quality measurement(s)=f(compression objective(s) and/or compression result 
measurement(s) and/or image characteristic(s)), and 
compression objective(s)=f(compression condition(s) and/or image 
characteristic(s) and/or quality measurement(s)). 
The principle is therefore to study the variations in the reconstruction 
error (RMS error or IQM) according to the compression ratio achieved and 
according to the statistical criterion. 
The statistical criterion varies on working through the test image base. It 
is then possible to see if the evolution of the error as a function of the 
criterion at constant compression ratio follows no particular law or, to 
the contrary, features a monotony and irregularity that can be interpreted 
and used to control the adaptive mode. 
The graphs can be used directly as digital data to predict the behavior of 
a given algorithm for a given image. 
In that they are already sufficiently regular, the graphs can be smoothed, 
for example using spline functions, to eliminate variations due to the 
test base used. 
The graphs also identify the algorithm deemed to be the best for a given 
image on the basis of the value of the statistical criterion or criteria 
used. 
2.3.2 Examples 
One or more of the following three-dimensional graphs, stated here by way 
of non-limiting example, can be used: 
RMS error=f(compression ratio, complexity of the set of image data), 
RMS error=f(compression ratio, entropy of the set of image data), 
RMS error=f(compression ratio, frequency power of the set of image data), 
psycho-visual quality=f(compression ratio, complexity of the set of image 
data), 
RMS error=f(compression ratio, size of the set of image data), 
RMS error=f(compression ratio, brightness of the set of image data), 
compression ratio=f(compression algorithm parameter, complexity of the set 
of image data), 
RMS error=f(compression algorithm parameter, complexity of the set of image 
data), 
compression ratio=f(complexity of the set of image data), 
compression ratio=f(entropy of the set of image data), 
etc. 
Note that the last two examples concern lossless compression algorithms for 
which the only problem is to predict the compression ratio that will be 
obtained for a given image. 
FIGS. 4 through 6 are examples of such three-dimensional graphs, after 
smoothing. They are graphs of the RMS error=f(compression ratio, 
complexity of the image) type. 
3. Selection Phase 
3.1 Requirements and Uses 
3.1.1 Example: Nine Parameter Setting Modes 
In the example described the adaptive mode contains nine parameter setting 
modes corresponding to possible compression objectives for a user. 
The objectives can be classified into three categories: 
1. Specifying directly the algorithm and its parameter or parameters: this 
objective concerns only persons having sufficient knowledge of the 
algorithm to wish to use it directly, so this type of parameter setting 
must continue to be available, but is evidently not the most interesting 
of those that an adaptive mode can provide, 
2. Specifying a parameter that is equivalent to a ratio: this concerns 
parameters that specify more or less directly the size of the file 
obtained after compression, whether this is via this size, the ratio (as a 
ratio or as a bit-rate), a transmission time at a given bit-rate, etc, and 
3. Specifying a parameter that is equivalent to a reconstruction error: 
this is a parameter that specifies more or less directly the error between 
the original image and the compressed image. It can be a keyword 
specifying the quality to be preserved, a digital error measurement to be 
achieved, etc. 
The parameter setting possibilities given to the user are listed below. 
However, the addition of a further possibility suited to a specific need 
always remains possible and a priori simple, as in principle all that is 
necessary is to translate this parameter into a target ratio or a target 
error. 
Specifying an Algorithm 
A1 algorithm and parameter: the algorithm is one of two available 
algorithms and the meaning and the field of definition of the parameter 
are related to it. 
Specifying a Compression Ratio or Equivalent Parameter 
T1 compression ratio: the compression ratio is the ratio between the size 
of the original image and that of the file after compression. This number 
must be greater than 1. 
T2 size of compressed file: this is the size of the file on leaving the 
compression algorithm. It must be less than the size of the original 
image. The size is specified in bytes, for example. 
T3 transmission time: this is the time to transfer the image across a 
network. The time is meaningless unless associated with a network bit 
rate. The two numbers must be greater than zero. The time is specified in 
seconds, for example, and the bit rate in bits per second, which 
correspond to the usual network conventions. 
Specifying a Reconstruction Error or Equivalent 
Q1 target RMS error: this is the RMS error required between the original 
image and the reconstructed image. This is a real number greater than 
zero. 
Q2 reconstruction quality: this involves specifying the quality of the 
image obtained after compression and decompression expressed by means of 
keywords. The keywords are: 
perfect, 
excellent, 
good, 
acceptable, 
poor. 
Q3 percentage quality preserved: this is a percentage for varying the 
output quality uniformly (and therefore continuously, unlike the above 
keywords) between 100% (impeccable quality) and 0% (maximal degradation of 
the image). 
Additionally the parameters T1, T2, T3, Q1, Q2 and Q3 can be associated 
with the requirement for an algorithm with which the specified objective 
must be achieved. In that a target rate or error can be achieved with any 
algorithm through uniform parameter setting, this does not raise any 
particular problem. In this case the native parameter of the algorithm is 
naturally not specified. 
Moreover, in the context of the embodiment described, specifying one of 
these objectives leads to updating of all the other fields to maintain the 
consistency of parameter settings in the various elements of the 
interface. This updating is effected: 
between parameters of the same kind (T1 to T2, etc), and 
between parameters of different kinds (A1 to T1, T1 to Q1 etc), using the 
three-dimensional graphs to evaluate either the RMS error obtained with a 
given algorithm and its parameter or the ratio obtained for the specified 
RMS error or vice versa. 
3.1.2 Automatic Compromise 
In addition to these usage scenarios, it is beneficial to have a 
parametering mode in which the user does not give any particular objective 
and for which the adaptive mode itself must find the best compromise 
between compression ratio and output quality. 
This compromise can consist in: 
selecting the process obtaining the best compression ratio for an output 
quality deemed to be satisfactory (for example corresponding to the above 
"excellent" or "good" criterion), and 
selecting the method obtaining the best ratio of the compression ratio 
obtained to the RMS error obtained by an overall optimization based on the 
graphs for all possible algorithms and all the possible modes of parameter 
setting. 
3.1.3 Conflict Management 
There are two types of parameter setting conflict: 
association of incompatible parameters: for example, specifying a 
compression ratio and a quality to be achieved for the compression of a 
given image, and 
in the context of focusing (see below), incompatible parameters between 
areas and the image background (typically, when the volume of data 
corresponding to the compressed areas no longer enables a particular 
compression ratio to be achieved for the whole of the image). 
3.2 Choice of Optimal Compression Conditions 
3.2.1 Reduction of User Parameters 
Before choosing the compression method proper, it is necessary to go from 
the objectives of the user to the two underlying parameters that can be 
controlled using three-dimensional graphs, namely a compression ratio or a 
reconstruction error expressed in the form of an RMS error. 
The following conversions are therefore effected: 
A1 algorithm and parameter: no transition is to be done, the objective is 
usable directly for parameter setting. 
T1 compression ratio: no transition is to be done, the objective is usable 
directly for parameter setting. 
T2 size of compressed file: depending on the algorithms, the number of 
bytes required for the compressed file can be used directly as a parameter 
(for example for EZW). 
T3 transmission time: from a transmission time t in seconds on a network 
and the bit rate D in bits per second of that network it is possible to 
deduce the target compression ratio Tx to be achieved for an image whose 
original size in bytes is known. 
Q1 target RMS error: no transition is to be done, the objective is usable 
directly for parameter setting. 
Q2 reconstruction quality: the five keywords below have been chosen in line 
with standard practise in the literature and so that each corresponds to 
one step in the perception of image quality. 
Perfect: no loss on compression. Implies the use of lossless compression. 
Excellent: no visible degradation in the image. There can be errors 
identifiable by digital measurements such as the RMS error, but a human 
observer viewing the image at its original size and resolution must not be 
able to detect them. 
Good: a user viewing the image attentively can make out the distortion, but 
the latter must remain discrete and non-irritating. 
Acceptable: clearly visible degradation can be seen without viewing the 
image attentively. The scene must remain recognizable. 
Poor: the scene is degraded overall, and is no longer necessarily 
recognizable. Only the general form remains and can possibly serve as a 
background to indicate the context in the context of focusing. 
Q3 percentage quality conserved: it is useful to be able to indicate the 
required output quality as a percentage. 
3.2.2 Choice of Algorithm 
When the objectives of the user have been reduced to obtaining a target 
compression ratio or a target RMS error, the algorithm to be used is 
determined by finding, for the image: 
if there is a target compression ratio, the algorithm that gives the RMS 
error for an image having the complexity of the input image, 
if there is a target RMS error, the algorithm that gives the highest 
compression ratio for an image having the complexity of the input image, 
or 
if global optimization is requested, the algorithm that gives the best 
compression ratio for a quality deemed acceptable (to be defined) or which 
gives the best ratio of the compression ratio obtained to the RMS error 
obtained. 
The RMS error obtained at a given compression ratio or the compression 
ratio associated with the target RMS error for the current image is 
predicted using the three-dimensional graphs: 
EQU RMS error=f(ratio, complexity) 
generated for each algorithm, as shown in FIGS. 4 through 6: 
if there is a target compression ratio look for the RMS error corresponding 
to the coordinates (ratio, complexity) on the graph, 
if there is a target RMS error, look for the value of the compression ratio 
to which is necessary to compress an image whose complexity is that of the 
input image to obtain this RMS error. 
The three-dimensional graphs are based on the experiments mentioned above 
and smoothed using spline functions. 
For algorithms using an inherent parameter different from the compression 
ratio and the RMS error (JPEG, FISHER, SHAPIRO) there is an alternative 
method using other three-dimensional graphs to sort the results or to take 
the method that gives the most precise estimates. In this case the 
following are used: 
EQU ratio=f(parameter, complexity) 
and 
EQU RMS error=f(parameter, complexity) 
To determine the RMS error corresponding to a required ratio for an image 
of given complexity the first step is to use the first three-dimensional 
graph to find the inherent parameter of the algorithm that must be used to 
obtain this ratio. The RMS error obtained with that parameter is then 
determined using the second three-dimensional graph. 
A similar procedure in employed to find the ratio corresponding to a 
required RMS error, using the second and then the first three-dimensional 
graphs in succession. 
4. Additional 
4.1 Three-dimensional Graph Optimization 
The experimental data on which the results described above are based, 
essentially in the form of the three-dimensional graphs used, were 
obtained and integrated manually. 
The three-dimensional graphs were determined from a base of 50 images and 
the quality criteria from visual tests carried out with 10 images, which 
corresponds to the desire for a prototype validating the principle of the 
adaptive mode and usable as a demonstration platform but is insufficient 
for robust use in an operational environment. 
Evolution of the system as a whole therefore presupposes integrating into 
it the process of generating and updating this data, in order to be able 
to: 
update the three-dimensional graphs with improved versions, resulting from 
new experiments or from ad hoc manipulation of their content (such 
improvements and updates must be of a kind that can be done by system 
designers and by users, in particular to enable users to produce 
three-dimensional graphs characteristic of images specific to their needs 
and of which they have large numbers), and 
add or modify user parameter reduction methods (network parameters, quality 
criteria, etc) to adapt to particular contexts of use. 
4.2 Focusing 
The selection phase can advantageously cooperate with a focusing step 39 
(FIG. 3) which can be presented as an independent "tool". Focusing in 
accordance with the invention consists in selecting one or more preferred 
areas in an image or in a sequence of images and requiring enhanced 
processing for those areas (or, conversely, degraded processing for the 
remainder of the images). 
For a given size of file, this makes it possible to obtain a very high 
image quality for the zone in question. This can be a particular element 
compared to the background of the image, for example, or the face (or just 
the mouth) of a speaking person in a videophone application. 
Two objective definition strategies can be implemented: 
at least one compression objective is set for the image portion or portions 
and/or at least one image in a sequence of images and/or an identical 
image portion in a sequence of images together with at least one 
compression objective for the remainder of the image data, or 
at least one compression objective is fixed for the image portion or 
portions and/or at least one image in a sequence of images and/or an 
identical image portion in a sequence of images and for the set of image 
data. 
The method advantageously further includes continuous visualization of the 
images during decompression with a sharpness increasing with the 
compression ratio. In this case focusing can also enable an area to be 
selected in the image being decompressed. Only this area will then be 
delivered with enhanced quality. 
4.3 Taking Account of Other Information 
In addition to the various aspects already considered, the operations of 
selecting compression conditions can be optimized by allowing for various 
ancillary parameters that are pertinent to the efficacy of at least some 
algorithms, such as the image size and format, the brightness of the 
image, etc. 
Other aspects can also be taken into account such as the compression and 
decompression times (and their ratio) and robustness vis-a-vis 
transmission errors. 
4.4 Improving Prediction Accuracy 
To improve prediction accuracy further it is possible to enhance the 
performance of the three-dimensional graphs by modifying them using 
various processes such as: 
extensions by continuity, or 
adding a positive or negative constant to favor or to defavor a particular 
algorithm. 
4.5 Toolbox 
The method of the invention can be implemented in the form of a toolbox 
made up of independent software entities that can cooperate, including: 
a selection tool (the reference functions can be predefined, adapted to the 
needs of the user and/or defined by the user, using their own test image 
bank), 
a plurality of compression algorithms (the choice of algorithms can be 
tailored to the needs of the user), 
a focusing tool, 
a continuous visualization tool, 
etc.