The goal of this paper is to propose a fast and secure multi-stage image compression-decompression system by using a wireless network between two Personal Computers (PCs). In this paper, the Principal Component Analysis (PCA) technique is used for multi-stage image compression and Inverse Principal Component Analysis (IPCA) for multi-stage image decompression. The first step of the proposed system is to select the input image, the second step is to perform PCA up to 9 times on the input image, this compression, and after multi-stage compression process then the third step begins by transforming across wireless Ad hoc Network (WANET) to the second computing device, forth step start with multi-stage decompression process up 9 times. The proposed system for different images is transferred over the wireless network using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of the MATLAB program. The proposed system implements 25 different images correctly (100%). The main contribution of this paper is that we are dealing with the black image at the end of the compressed process ad start with a black image at the start of the decompressed process of this proposed system. In this work, the compressed and uncompressed images are compared with each other in their size and transmission time. This system can be very useful in networks because they provide a high level of protection to the transmitted data from hackers because they cannot guess how much the image has been compressed or what kind of information the image represents.
With the fast development of the Internet, the use of high-resolution image transmission is increasing due to vast volumes of digital images being shared by people over the Internet every day. High-resolution images need greater bits for storage and transmission because the information amount in the digital image is very large, and it occupies a large number of resources. For these reasons, efficient transmission of digital images has become a major challenge in the field of information technology. Thus, it is important to use an appropriate method to compress images in order to store and transmit them efficiently. Taking into consideration, the bandwidth consumption over multimedia communication, having an effective compression coding technique is necessary to enable the effective and rapid transmission of image data over the network [
In the image processing, there is one particular field by which the size of data can be reduced called Image Compression. These techniques use various mathematical models in order to minimize irrelevance and redundancy of image data so that it can be stored or transmitted efficiently. Image compression can be lossy or lossless. Lossless compression is generally preferred for backup storage e.g. medical picturing. Whereas Lossy methods are specially used for natural images e.g. personal digital images. One of the lossy compression techniques is PCA. PCA is the best image compression technique among many of the possible approaches used to reduce image file size at the expense of its perceptual quality because other lossy compression standards such as JPEG offer a tradeoff between image quality and file size, while in lossless compression techniques such as run-length encoding, no information reduction takes place, but the image file size may remain large [
In the field of communication between devices, networking is the most important area which is responsible for a source to destination transmission of data. The network is established using air as a transmission medium and there is no physical path between two nodes of the network such networks are called Wireless Networks. Wireless Adhoc Networks are networks in which nodes can communicate with others by wireless transmissions without the help of a centralized coordinator. WANET is becoming very popular to connect with each other within a particular region for the various applications. As this field as growing up very rapidly, it comes with new issues such as securing the network that has been one of the major concerns in today’s world [
In this research, we have proposed a system that compresses/decompresses digital images multiple times by using the PCA/IPCA technique. The compressed images are transferred over the wireless network using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of MATLAB program. For more security, PCA/IPCA method could be used also to secure image transmission against attackers over a wireless network because they can’t know what type of information the compressed image is holding. This project deal with 25 digital images samples from Air Freight dataset because the samples do have significant lighting, specular effects, so they are more realistic than some other image samples might be. This work is expanded on the previous work, which was presented by [
This paper consists of 4 sections, the first is the introduction, the second section is proposed methodology, while the third section is experimental results, and the last is conclusions.
The proposed methodology for the multi-stage PCA/IPCA method is discussed in the following sections includes a brief discussion of the quality measurements and the proposed wireless network connection and transmission process.
Principle Component Analysis is a multivariate statistical method used to describe variability among observed, PCA depending on the idea of correlated variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors, plus “error” terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset [
The goals of PCA are included in the following [
1) Extract the information (features) from the datasets.
2) Compress the size of the dataset by keeping only the related information.
3) Decompress the compressed dataset without losing information.
4) Analyze the structure of the observations and the variables.
In the beginning, one-stage image compression/decompression must be expounded as shown in
Principal Component Analysis (PCA) is used as a dimension reduction method by reducing the number of dimension with low errors. Firstly, the RGB image from Air Freight dataset must be converted to greyscale image named as (I) and used in MATLAB code, the grey image format is row × column matrix. By using Air Freight images, the dimension of the images is 160 × 120. The grey image matrix is centred by computing the mean of the image matrix,
m e a n I ( 120 ∗ 160 ) = m e a n ( I )
Then subtracting each pixel gray value from the mean gray value:
s u b I = I − m e a n I
After that, the covariance matrix of the subtracted matrix must be computed in order to determine the eigenvectors (principal components) of the covariance matrix and collect them into the projection matrix.
cov = c o v a r i a n c e ( s u b I )
[ V D ] = e i g ( c o v )
V t r a n s ( 160 ∗ 160 ) = t r a n s p o s e ( V )
Each of those eigenvectors is associated with an eigenvalue, which is the size or dimension of the corresponding eigenvector. The eigenvalues are arranged in descending order and the eigenvectors are chosen corresponding to the largest eigenvalues. Finally, the final matrix is constructed from the selected eigenvectors:
s u b I t r a n s = t r a n s p o s e ( s u b I )
f i n a l m a t r i x ( 120 ∗ 160 ) = V t r a n s ∗ s u b I t r a n s
The final image matrix is decompressed by performing Inverse Principal Components Analysis (IPCA) on the compressed image as shown in
I f i n a l m a t r i x t r a n s = i n v ( V t r a n s ) ∗ f i n a l m a t r i x
The decompressed image matrix is constructed by adding the mean of the image matrix to the transpose of the multiplication [
I f i n a l m a t r i x ( 160 ∗ 120 ) = t r a n s p o s e ( I f i n a l m a t r i x t r a n s ) + m e a n I
A new algorithm is proposed to compress the image at different stages, it’s a sequence of image compression stages. With each stage, there is a new different compressed image that has a different size. These sequences of image compression stages are corresponding to the decreasing of image sizes. The process is called the forward multi-stage image compression. The reverse direction or the backward multi-stage image compression contains a sequence of image decompression stages, which are corresponding to the increasing of image sizes. The images are compressed N times as shown in
The proposed network system consists of two PCs, network’s PCs transfer data by using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of MATLAB program. TCP requires only the destination’s IP address and port number for the data to be transferred to that destination. Client and server TCP sockets are programmed using the network role property on the TCP/IP interface that supports a single remote connection. To communicate over TCP/IP interface, TCP/IP object must be created before transmission with remote host’s IP address and port number. The Port number must be a positive integer between 1 and 65,535. The typical workflow for TCP/IP server and TCP/IP client sessions is shown in
network transmission takes place. Static IP addressing is applied to the proposal network because it provides more reliability than a dynamic IP address. The network transfers compressed and input images into one line. First, PC1 sends the chosen image to PC2 (which is either a compressed image or input uncompressed image). Then PC2 receive either compressed image or input uncompressed image, then decompresses the compressed image. In this network, Acer AOD257 CORE i3 notebook and Toshiba Portege M780 CORE i5 computers were used for image transmission [
The system is tested by using 25 image samples from Air Freight dataset. These images must be converted to grayscale in order to employ a multi-stage compression process. In this system, PC1 is the server that writes the image to the client’s buffer and PC2 is the client that reads the image from the server. In PC1 (the server), the gray scale input image is gone through 9-stage of compression as shown in
The size of compressed Air Freight images is shown in
| Image No | Transmit Time | Receive Time | Total Time (before compression) (sec) | Transmit Time (after compression) | Receive Time (after compression) | Total Time (after compression) (sec) | Size before compression (kb) | Size after compression (bytes) | Size after decompression (kb) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 1.9 | 0.1 | 2.0 | 0.3 | 0.4 | 0.7 | 29 | 333 | 11 |
| 2 | 1.6 | 0.1 | 1.7 | 0.3 | 0.4 | 0.7 | 29 | 340 | 11 |
| 3 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 368 | 11 |
| 4 | 1.8 | 0.1 | 1.9 | 0.2 | 0.3 | 0.5 | 29 | 338 | 11 |
| 5 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 348 | 11 |
| 6 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 319 | 11 |
| 7 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 351 | 11 |
| 8 | 1.5 | 0.1 | 1.6 | 0.2 | 0.3 | 0.5 | 29 | 327 | 11 |
| 9 | 1.4 | 0.1 | 1.5 | 0.2 | 0.4 | 0.6 | 29 | 348 | 11 |
| 10 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 349 | 11 |
| 11 | 1.8 | 0.1 | 1.9 | 0.2 | 0.4 | 0.6 | 29 | 350 | 11 |
| 12 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 29 | 352 | 11 |
| 13 | 1.7 | 0.1 | 1.8 | 0.2 | 0.3 | 0.5 | 29 | 368 | 11 |
| 14 | 1.8 | 0.1 | 1.9 | 0.2 | 0.4 | 0.6 | 30 | 344 | 11 |
| 15 | 1.8 | 0.1 | 1.9 | 0.2 | 0.3 | 0.5 | 30 | 368 | 11 |
| 16 | 1.9 | 0.1 | 2.0 | 0.2 | 0.3 | 0.5 | 30 | 345 | 11 |
| 17 | 1.9 | 0.1 | 2.0 | 0.2 | 0.3 | 0.5 | 30 | 362 | 11 |
| 18 | 1.7 | 0.1 | 1.8 | 0.2 | 0.3 | 0.5 | 29 | 386 | 11 |
| 19 | 1.9 | 0.1 | 1.9 | 0.2 | 0.3 | 0.5 | 29 | 317 | 11 |
| 20 | 1.8 | 0.1 | 1.9 | 0.3 | 0.4 | 0.7 | 27 | 324 | 11 |
| 21 | 1.6 | 0.1 | 1.7 | 0.2 | 0.4 | 0.6 | 27 | 314 | 11 |
| 22 | 1.7 | 0.1 | 1.8 | 0.2 | 0.3 | 0.5 | 27 | 327 | 10 |
| 23 | 1.7 | 0.1 | 1.8 | 0.2 | 0.3 | 0.5 | 27 | 381 | 10 |
| 24 | 1.5 | 0.1 | 1.6 | 0.2 | 0.3 | 0.5 | 27 | 365 | 10 |
| 25 | 1.7 | 0.1 | 1.8 | 0.2 | 0.3 | 0.5 | 26 | 371 | 10 |
input image size. This means that there is information loss which is about 62% of the original size without losing the important features of the original image. The time factor is also affected by the compression. In this project, image transmits and receive time are measured during opening and closing of sockets. When comparing the total time of image transmissions before and after multi-stage compression, a decline in time is shown in
The main contribution of this paper is that we are dealing with the black image at the end of the compressed process ad start with a black image at the start of the decompressed process of this proposed system.
All implementation results of the proposed system as shown in
The authors declare no conflicts of interest regarding the publication of this paper.
Ibrahim, A.A., Khalaf, T.A. and Ahmed, B.M. (2022) Multi-Stage Image Compression-Decompression System Using PCA/IPCA to Enhance Wireless Transmission Security. Journal of Computer and Communications, 10, 87-96. https://doi.org/10.4236/jcc.2022.104008