Adaptive Image Compression and Identication for
Virtual Conferencing
Jiangfeng Ding Edwin H.-M. Sha
Department. of Computer Science and Engineering
University of Notre Dame
Notre Dame, IN 46556
Email:
fjding,[email protected]Tel:
(219)631-
8720Fax:
(219)631-
9260Abstract
Image compression has been an important research topic for many years. There are many kinds of compression techniques, but most of these do not fully take advantage of the charac-teristics of a target application. An example is virtual conferencing, one of the most popular applications in multimedia, which demands a huge network bandwidth. The paper presents an application-specic image compression technique focusing on virtual conferencing. The paper proposes a compression technique that compresses the image background and foreground (the face part) with dierent compression qualities. Compared to standard JPEG compression, our algorithms may reduce half of the image le size with a reasonably clear quality of background. This paper discusses how to divide an image into a background part and a foreground part au-tomatically, and how to design and compare several dierent algorithms for compressing these two parts with dierent quality.
Keywords-Background Dilution, DCT, Quantization, Foreground Capture, Virtual
Con-ferencing.
1 Introduction
Uncompressed images require considerable storage capacity and very high bandwidth in transfer. In order to manage large image data objects eciently, these objects need to be compressed to reduce the le size. Compression tries to eliminate redundancies in the pattern of data [1] [6].
Many studies have shown that users at a computer have a patience factor ranging from two to four seconds. Even though network speeds have been increasing consistently, very large image objects can take as much as a couple seconds to transmit. Given just a few seconds to retrieve, transmit, and display an image, improving the eciency of storage and transmission of data objects is of paramount importance to image systems.
Most solutions for this problem focus either on increasing network bandwidth by using a better network, using a better compression algorithm or employing other techniques to get higher perfor-mance [2,4,7,8]. We propose a compression approach which takes advantage of the characteristics of the target application. The size of the compressed information using this approach should be much less than the one using a standard approach, but the quality of the result has little eect on the users of the application. This paper will use virtual conferencing to show how we can give a better solution.
Virtual conferencing is one of the most important applications in multimedia. It requires huge real-time network bandwidth. Any technique which can reduce the requirement of the bandwidth is useful. As a joint research project with a major telephone and Internet company, the research is motivated by the future possible placement of digital cameras equipped with telephones in public areas such as airports and stations. The background part might be quite complicated and changing frequently but the ne details of these backgrounds are not important to the participants of a virtual conference session. However, the foreground (the face part) should be seen as clearly as possible. The compression techniques should take advantage of the dierent clarity requirements for these two parts.
Though there are many dierent compression techniques for image compression, most of them compress the whole image with the same compression quality [5]. But there are some applications, such as virtual conferencing, in which we care not much about the background, but do considerably care about the bandwidth requirement. So if we can separate the image into two parts{ background and foreground{and compress the background and foreground with dierent compression qualities, then the image le size will be much smaller and the bandwidth requirement will also be much smaller [3]. We call the procedure of compressing the background with lower quality background dilution by us.
In this paper, we will present how to automatically divide the image into a foreground part and a background part and how to compress the background and foreground with dierent com-pression qualities. We do our research work on the model of JPEG comcom-pression. The key point of the procedure is that the background dilution should be done fast. It should not put too much computation overhead on the JPEG compression. Dierent compression algorithms are discussed in this paper, each having some amount of tradeos. Some compression algorithms presented in the paper do not require the revision of the decompression part of the JPEG package (aka djpeg), so the clients have no knowledge that the compressions were done in a dierent way. This is especially useful for camera systems that are pre-installed in a public area. These systems can transmit their images to a client who can then use a standard package to decompress the data. Other proposed algorithms in the paper require some revisions on the djpeg program, but they usually result in a better compression ratio. Experimental results show that the le size of images compressed by our algorithm is only about half of those compressed by standard JPEG compression when about 1/4 of the image is the foreground, and most of the details in the background still can be seen.
We call this image compression scheme Application Specic Image Compression for Virtual Conferencing. This paper is organized as follows: In Section 2 we describe the procedure of a standard JPEG compression algorithm. In Section 3 we present our compression algorithms. In Section 4 we will talk about how to capture the foreground from an image. Experiments and results are presented in Section 5.
2 Basics
A simple characterization of data compression is that it involves transforming a string of characters in some representation into a new string which contains the same information but whose length is as small as possible. There are several typical transformations employed in image compression, such as the Fourier Transformation and the Wavelet Transformation. Besides transformation, most of the image compression algorithms also involve in some kind of quantization and encoding [1]. Our algorithms of background dilution apply to this class of compression algorithms. In this paper we use JPEG as an example to show how to modify JPEG to implement background dilution.
JPEG is a sophisticated and exible standard for compressing and storing still images. JPEG denes both lossy and lossless compression processes. The lossy processes are based on the DCT (Discrete Cosine Transformation), whereas the lossless processes are based on forms of DPCM (Dierential Pulse Code Modulation). The method in Lossy JPEG depends on an important mathematical and physical theme: local approximation. In our research, we focused our attention on JPEG lossy compression.
The JPEG compression scheme utilizes forward DCT (or the forward DCT mathematical func-tion), a uniform quantizer, and entropy encoding. The DCT function removes data redundancy by transforming data from a spatial domain to a frequency domain; then the DCT coecients are quantized with weighting functions to generate a new set of coecients optimized for human eyes. Finally the entropy encoder minimizes the entropy of the quantized DCT coecients [6] [9].
8 X8 blocks
Source Image
FowardDCT Quantizer Entropy Encoder Compressed Image DCT-BASED Encoder
Quantization Table
Encode Table
Figure 1: Lossy JPEG encoder model
There are four major steps for JPEG compression: Image preparation, Calculating the discrete cosine transformation (DCT) of each block, Quantization (round o the DCT coecients according to quantization matrix), and Zig-zag ordering for entropy encoding [6].
3 Background dilution
For this problem we have dened several dierent schemes to solve it. One method we may use is independent compression, which separates the foreground and background of the image and then compresses them independently. We may compress the foreground with a high quality and compress the background with low quality. The advantage for this scheme is that it is easy to implement, but the problem is that it is hard to combine the two parts and there is too much computation overhead during image combination. Since both the data process time and image size are very
important for applications such as virtual conferencing, it is not a good scheme for our application. A better method is to do the background dilution during image compression.
As we discussed in section 2, there are four steps in JPEG compression: image preparation, discrete cosine transformation, quantization and entropy encoding. We may do background dilution after the rst three steps of JPEG image compression. To do this, we suppose that the image has already been divided into a background part and a foreground part, which means that we know the boundary of the foreground part. We will discuss how to identify foreground from the image in section 4.
3.1 Background dilution after image preparation
The rst step of JPEG compression is image preparation, which reads in the source image, breaks it down into components and divides the components into
8
8
blocks for the purpose of computingthe DCT.
We can do background dilution by using the average value of each
8
8
block to represent thevalues of the 64 pixels of the block in the background. By doing this approximation, if we apply DCT to this
8
8
block, there is only one value left (the DC value) in the DCT output matrix. Allother values will be zero. We call this algorithm the AVG-Algorithm.
At the image preparation step, JPEG loads the source image and divides the image into 8 X 8 blocks. The AVG-Algorithm keeps all the pixel values for each
8
8
block in the foreground partof the image, computes the average value of each
8
8
block in the background part and use thisaverage value to approximate all 64 values in the
8
8
block. After doing this approximation, wethen feed the block stream to the DCT.
Background dilution is easy to implement after image preparation and has little computation overhead. The algorithm goes through each
8
8
block once. So the computation overhead isO
(h
w
), whereh
is the height of the image andw
is the width of the image. But there is onlyone xed compression quality for the background. In some applications, the user may want to have dierent qualities of background according to the current network bandwidth and bandwidth demands, so only one level of background quality could be a problem. Another advantage of the AVG-Algorithm is that the JPEG image format does not change, so the user may use any standard JPEG to decompress the images compressed by the AVG-Algorithm.
3.2 Background dilution after DCT
The second step of the JPEG compression is DCT, which transforms the image from the spatial domain to the frequency domain. The input to the DCT is a stream of
8
8
blocks, which arethe pixel values of the 64 pixels in each block. The output from DCT is an
8
8
DCT coecientmatrix, which represents the frequency of the pixels. By doing this transformation, it separates the image into parts of diering importance, with respect to the image's visual quality. The upper left values of the block represent the low frequency and are more important to the human vision. The lower right values of the block represent the high frequency and are less important to human vision.
the foreground part, we use high quality compression, which means we keep all 64 values for the
8
8
block; (2) For the background part,we selectively set some less important values in the8
8
matrix to zero according to some algorithms. After making this change, we may feed the stream of the DCT coecient matrix to the quantization stage. With this compression technique, users may specify the quality of the background needed. We call this algorithm the Keeping Algorithm (K-Algorithm). This algorithm keeps the values of top-left sub-block of each
8
8
block,and setsall the remaining values in the
8
8
block to be zero, as shown in Figure 2. We call the top-leftsub-block a critical sub-matrix. The larger the critical sub-matrix is, the higher the background quality will be. Under this general idea, we have created several modications to the K-Algorithm. The Triangle Keeping Algorithm (TK-Algorithm) is similar to the K-algorithm, but we only keep the values of the top-left triangle of each
8
8
block (Figure 2). As we discussed in section2, the last step of JPEG compression is Zig-zag ordering. Because of this ordering, the triangle keeping method will create a longer sequence of consecutive zeroes.
set to zero set to zero
keep All values here critical DC AC AC Keep critical DC AC AC 8 X 8 DCT MATRIX K-Algorithm 8 X 8 DCT MATRIX K-Algorithm T submatrix submatrix
Figure 2: Digram for K and TK algorithms
The Threshold Algorithm (TS-Algorithm) rst chooses some frequency value according to a desired background quality (we call this value the threshold), and sets any DCT coecients in the
8
8
matrix with magnitudes below the threshold to zero . This algorithm tries to throw away allthose small frequency values, since small values may have less inuence to the image quality. The Keeping-and-Scaling Algorithm (KS-Algorithm) keeps the values of the upper-left sub-block (the critical sub-matrix) of the
8
8
DCT coecients, and scales down the remaining DCTcoecients as show in Figure 3. As we just talked, the upper-left corner of the DCT coecient matrix represents the low frequency in the image and the lower-right DCT coecient represent the high frequency change, which are less important to human vision. The Triangle Keeping-and-Scaling Algorithm (TKS-Algorithm) keeps the values of DCT coecients according to zig-zag order. Scaling-and-Keeping Algorithm (SK-Algorithm)keeps the DC value of each DCT coecient matrix, scales down the other values in the upper-left sub-block (the critical sub-matrix) of the DCT coecient matrix and sets all the remaining DCT coecients to zero (Figure 3).
The advantage of background dilution after DCT is image format independence and high e-ciency. And like background dilution during image preparation, we do not need to change JPEG image format at here. So the user may use any standard JPEG to decompress the images
com-AC critical
DC AC 8 X 8 DCT MATRIX
keep All values here
Scale down the values here DC AC AC 8 X 8 DCT MATRIX KS-Algorithm T
Scale down the values here critical matrix Keep KS-Algorithm matrix AC critical DC AC 8 X 8 DCT MATRIX matrix value here Scale down the
Set zero to values here keep
SK-Algorithm
Figure 3: Digram for KS ,TKS and SK algorithms pressed by these algorithms.
3.3 Background dilution after quantization
Another option is doing the background dilution during quantization. Quantization is a process that determines what information can be safely discarded without a signicant loss in visual delity. It is implemented by dividing the 63 AC values of the DCT coecient matrix by a quantization matrix and then rounding o some bits in the data representation. So the values of the quantization matrices decide the quality of an image. So the idea is that we may use dierent quantization matrices for the background and for the foreground. The values in the quantization matrices for the background are larger than those for the foreground.
The Quantization Algorithm (Q-Algorithm) uses dierent quantization tables for the back-ground part and the foreback-ground part. We store the quantization tables for both foreback-ground and background in the JPEG image header. The Q-Algorithm also must store the boundary of the foreground in the image header. This means that unlike the background dilution after DCT or after image preparation, dilution after quantization has to change JPEG image format. Therefore the users may not use the standard JPEG function to decompress the image compressed by the Q-Algorithm. Since JPEG is a broadly used standard, and we want to keep compatibility with its standard image format, changing the image format is a poor option. But this compression scheme can achieve a higher compression ratio and higher image quality.
The Quantization Modication Algorithm (QM-Algorithm) makes some modications to the Q-Algorithm. This algorithm does not use individual quantization tables for background and foreground. It only keeps one set of quantization tables. As we compress a image, if a block is in the foreground then we use the stored quantization tables. If a block is in the background, then we multiply the tables by some constant, create scaled tables and then use this modied value as the background quantization tables. When the users want to decompress the image, the QM-Algorithm uses the stored quantization tables to decompress the foreground and uses the scaled tables to decompress the background. A problem arises in this scheme because we need to have some way to let the de-compressor know how much the quantization tables were scaled and what are the boundaries of the foreground. This may be implemented by storing this information in the compressed image header or, for virtual conferencing, the sender and the receiver may have some
handshaking for this type of information before the images are sent.
The advantages of this scheme are low computational overhead and image format independence. But it is again necessary to change the JPEG image format to keep several quantization tables .
4 Foreground capture
4.1 General ideas
In this section, we discuss how to identify the foreground and the background of an image quickly. The user may specify the foreground part, but in many cases the foreground part would be better determined by a program automatically. Especially, for some applications, when the foreground part will change from time to time, such as in virtual conferencing, where the person in the meeting will move or change his position from time to time, so it is crucial that the software can decide the foreground part automatically. The problem of foreground capture is not easy. We design a algorithm and the experiments show quite satisfactory results. We call this problem \Foreground Capture".
Our algorithm is based on image comparison. Just before the virtual conferencing begins, the camera will take photo of the meeting area. We call this photo the environmental image (ENG). It is stored in the sender's image database. And after the meeting group enters the conferencing room, the camera will take photos (we call it IMG) and after the image IMG arrives at the sender, the sender will compare the image IMG with the environmental image ENG. We also suppose that the camera is xed. Ideally, if there is no change, these two photos will be the same, and we may regard that all the change is in the foreground part, i.e, if the pixel in the image IMG is dierent from corresponding pixel in the environmental image ENG, we may regard that the pixel lies in the foreground, else we may regard that the pixel is in the background.
But in reality, it's almost impossible. Because pixel values of the same object from two images may have big dierence even exactly the same object, due to the brightness , luminance change or some other unknown factors of the camera. We should allow that the pixel values may have some change between background parts in image ENG and IMG. So a problem is how to decide how much dierence to allow between background parts of the two images. We call this dierence the threshold. If the dierence between two blocks from image ENG and IMG is less than the threshold, we regard these two blocks as having the same pixel value, i.e the block is in the background. But if the dierence between two blocks is larger than the threshold, we regard these two blocks have dierent values, i.e the block is in the foreground.
Values of each pixel may have a huge change between two images, even from the same object. But the change of the average value of a block may be much smaller if they are exactly from the same object. So we should not compare values pixel by pixel. We should compare the average values block by block.
4.2 Decide the threshold automatically
A xed threshold wont work well for dierent environments. We design a way to decide the threshold automatically. We assume there must be several continuous blocks in the image that do
not fall into the foreground, i.e. there must exist some parts of reasonable size at the left side or the right side of the image that have the \same" values. We call this block the \sample region". We compute the dierence of pixels in the \sample region" of the ENG and IMG. Then we compute the accumulative distributions of these dierence. If the threshold found is not good, then we may reduce the sample region and try again or change the position of \sample region".
4.3 Foreground capture algorithm
IMG is the image taken during the meeting. We try to identify the foreground part in the IMG by comparing ENG and IMG. At rst, we load two images: ENG and IMG and divide them into
8
8
blocks. Then we compare the avarage values block by block. If the dierence is less than thethreshold, then we say that the two blocks are the same, i.e, the block falls into the background part, else we say these two parts are dierent, i.e, it falls into the foreground part.
There are always some blocks of the foreground that are similar to the corresponding blocks in the background, and there are always some blocks of the background which are quite dierent that are regarded as foreground. We call these blocks noise. This noise will cause holes in the background and foreground. We will talk about how to eliminate these in the following subsection.
4.4 Noise elimination
We suppose that there is only one foreground part. All the others are regarded as background. First we will go through all the blocks and nd all the continuous background parts and foreground parts. We call them islands. Generally, after this step, there will be many islands. Then we sort all the islands by their areas. We regard the largest two islands as the background and foreground, and call them host islands. All the other islands are regarded as the noise islands. Then we may merge the noise islands according to their neighbor islands.
5 Experiments and results
We did many experiments on several images. Here we present some typical results. Suppose the original image is 320 pixels in width and 240 pixels in height. So in RGB model, the original image size is 230415. In this case the foreground is 112 pixels in width and 160 pixels in height. So the size of the foreground is 53760 pixels, which is about 23% of the whole image.
Table 1 is the size comparison between images compressed by original JPEG compression and by the AVG-Algorithm with dierent compression qualities. We nd that images compressed by the AVG-Algorithm are only about half of the le size of images compressed by original JPEG when 1/4 of an image is foreground. We also nd that the AVG-Algorithm gets a better result when image compression quality is high. The problem for the AVG-Algorithm is that only one xed background quality is allowed. The users are not allowed to change the background quality.
Table 2(a) is the size comparison between the K-Algorithm and the TK-Algorithm with dierent sizes of the critical matrix. The rst column (column titled by \CS") is the size of critical sub-matrix.
CS
=1
means we only keep the DC value.CS
=8
means we keep all the8
8
valuesq=25 q=50 q=75 q=100
Original JPEG 6262 9252 13685 73855
AVG-Algorithm 3555 4681 6364 31255
percentage 56 50 46 42
Table 1: AVG-Algorithm, size comparison with dierent compression qualities
CS K-algo Tk-algo 1 5980 5980 2 8554 7636 3 11042 9367 4 12511 10919 5 13197 12102 6 13494 12981 7 13599 13421 8 13685 13575 (a) TSP TS-algo 0 5980 1 6542 2 6637 3 6891 4 7202 5 7657 6 8529 7 10000 8 13685 (b)
Table 2: Results of (a)K-Algorithm, TK-Algorithm; (b)TS-Algorithm
the image size of the TK-Algorithm is much smaller than that of the K-Algorithm but the image quality of the K-Algorithm and the TK-Algorithm are quite similar. The reason for this is that the TK-Algorithm creates a longer zero-sequence than the K-Algorithm does and those high frequency values kept by the K-algorithm do not have too much inuence on the image quality.
Table 2(b) is the result of the TS-Algorithm. In our experiment, the threshold is decided by
min
+(max
-min
)TSP=8
, wheremin
is the minimum DCT coecients of the8
8
matrix;max
is the maximum DCT coecients of the DCT matrix and TSP is the threshold parameters, which is greater than or equal to zero and less than or equal to 8 in our experiments.Table 3 is the results of the KS-Algorithm. The rst column is the size of the critical sub-matrix. Size i means that after DCT, it keeps the DC value and the other
i
2-
1
values in the upper lefti
i
corner of DCT coecient matrix. All the other values in the DCT coecients matrix arescaled down by some scalar. We use 1, 2 4 8,32 64 ,128 and 256 for scalars. The aim for these is that shift operation is fast. So it may save much computation time.
Table 4 shows the results of the TAlgorithm. The TAlgorithm is quite like the KS-Algorithm, except that the TKS-Algorithm keeps the upper left values according to Zig-zag order. So the TKS-Algorithm keeps fewer values than the KS-Algorithm. But what is discarded by the TKS-Algorithm are some high frequent values. So although the image size compressed by the TKS-Algorithm is smaller than that of the KS-Algorithm, the image quality is similar.
Table 5 shows the results of the SK-Algorithm. The rst column is the size of the critical sub-matrix. Size i means that after DCT, we keep the DC value and scale down the other
i
2-
1
CS S=1 S=2 S=4 S=8 S=16 S=32 S=64 S=128 S=256 1 13685 10689 8707 7429 6637 6227 6055 5985 5980 2 13685 11327 9913 9134 8740 8598 8558 8554 3 13685 12135 11397 11126 11074 11051 11042 4 13685 12843 12602 12537 12520 12513 12511 5 13685 13297 13227 13193 13195 13197 6 13685 13536 13521 13498 13494 7 13685 13615 13612 13602 13599 8 13685 13685
Table 3: Results of KS-Algorithm
CS S=2 S=4 S=8 S=16 S=32 S=64 S=128 S=256 1 10689 8707 7429 6637 6227 6055 5985 5980 2 11117 9518 8541 7988 7737 7658 7636 3 11641 10473 9821 9497 9374 9367 9367 4 12203 11459 11100 10951 10927 10919 5 12707 12296 12142 12018 12102 6 13155 13023 12995 12981 7 13451 13424 13421 8 13580
Table 4: Results of TKS-Algorithm zero in the DCT coecient matrix.
Combining these algorithms, we can oer all kinds of background qualities up to the foreground quality. Another modication is the TSK-Algorithm. The relationship between the TSK-Algorithm and the SK-Algorithm is the same as that of the K-Algorithm and the TK-Algorithm. The image size of the TSK-Algorithm is also smaller to that of the SK-Algorithm and the image quality is quite similar. The reason is the same as before. We will not put the results of the TSK-Algorithm here.
Table 6 shows the results of the QM-Algorithm. We apply the QM-Algorithm to the TK-Algorithm. The rst column is the size of the critical sub-matrix in the TK-TK-Algorithm. Others are dierent scalars to the JPEG luminance quantization table and the chronomance quantization table. The compression quality for the foreground is 75.
We list some representative results with le size less than 8800 bytes from each algorithm and compare their quality in Table 7. The images may be found at
http
:==www:nd:edu= jding=research=images:html
.The last column of the Table 7 is the qualities of the images. From this table, we know that our algorithms may cut half the size of the image le if about
1=4
of the image is the foreground and about3=4
of the image is the background. As we can see from the images and the size of the image les, the QM-Algorithm achieves much better result than any other algorithms. But it has to change JPEG image format, so the users may not use the standard JPEG to decompress the images compressed by the QM-Algorithm. If the change of the standard JPEG decompression is not a concern, the QM-Algorithm is our recommendation. All the other algorithms do not change JPEGCS S=1 S=2 S=4 S=8 S=16 S=32 S=64 S=128 S=256 1 5980 5980 5980 5980 5980 5980 5980 5980 5980 2 8554 7789 7172 6695 6343 6142 6033 5985 5980 3 11042 9398 8132 7205 6575 6228 6055 5985 5980 4 12511 10215 8512 7357 6605 6228 6055 5985 5980 5 13197 10531 8641 7417 6634 6227 6055 5985 5980 6 13494 10614 8677 7418 6637 6227 6055 5985 5980 7 13599 10667 8706 7422 6637 6227 6055 5985 5980 8 13685 10689 8707 7429 6637 6227 6055 5985 5980
Table 5: Results of SK-Algorithm
CS QM-algorithm Luminance/Chronomance scalor 1/1 2/2 4/4 8/1 8/8 16/1 32/1 1 5980 5807 5678 5746 5698 5675 5596 2 7636 7025 6516 6589 6222 6336 6112 3 9367 8219 7296 7366 6686 6882 6527 4 10919 9230 7871 7854 6973 7183 6730 5 12012 9927 8238 8059 7135 7268 6784 6 12981 10351 8382 8086 7158 7280 6788 7 13421 10515 8431 8114 7164 7297 6806 8 13575 10548 8441 8117 7165 7305 6809
Table 6: Results of QM-Algorithm
Algorithm Sample Image Size Percentage Quality
K-Algorithm K 2.jpg 8554 63% Good TK-Algorithm TK 2.jpg 7636 56 % Fair TS-Algorithm TS 5.jpg 7657 56% Fair KS-Algorithm KS 1 4.jpg 8707 64% Worse KS-Algorithm KS 2 32.jpg 8598 63% Good TKS-Algorithm TKS 2 32.jpg 7737 57% Fair SK-Algorithm SK 3 4.jpg 8132 59% Worse QM-Algorithm QM 16 1 3.jpg 6882 50% Good QM-Algorithm QM 4 4 2.jpg 6516 48% Fair QM-Algorithm QM 8 8 2.jpg 6222 45% Fair
image format but get a lower compression ratio. Images compressed by the K-Algorithm and TK-algorithm had similar qualities but the latter one achieves a higher compression ratio. But there are a limited number of background qualities in the (T)K-Algorithm(equal to DCT block size, which is 8 in JPEG). The (T)KS-Algorithm and (T)SK-Algorithm have more ne grained background qualities, and so are more exible. Among these algorithms, we recommend TKS-Algorithm to be used because it provides more levels of background quality than TK-Algorithm.
6 Conclusion
Image compression has been a hot topic for many years. The size and quality of the compressed image are two of the most important problems in this topic. There are all kinds of compression techniques. But all these algorithms compress the image with same compression quality. In this paper, we proposed a compression technique that compresses the image background and foreground with dierent compression qualities. As was shown in the experimental results, compared to stan-dard JPEG compression,our algorithms may reduce half of the image le size when 1/4 of the image is foreground.
Our research is based on the JPEG compression technique. It's one of the most commonly used image compression techniques used today.
Several dierent background dilution schemes are presented in this paper. The easiest and most direct one is background dilution after image preparation. But this scheme can only achieve a xed background quality. Background dilution after DCT is another scheme we used. There are many dierent background dilution algorithms under this scheme. We presented some algorithms and experimental results . The advantage of this scheme is that the compressed image format is not changed. Any clients may use standard JPEG to decompress the image compressed by our algorithms. The last scheme is doing background dilution during quantization. This algorithm may achieve a higher compression ratio and higher background quality, but it has to change the compressed image format, so the clients may not use standard JPEG to decompress the image compressed by our algorithm.
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