Efficient payload communications for IoT-enabled ViSAR vehicles using discrete cosine transform-based quasi-sparse bit injection
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High-performance remote sensing payload communication is a vital problem in air-borne and space-borne surveillance systems. Among different remote sensing imaging systems, video synthetic aperture radar (ViSAR) is a new technology with lots of principal and managerial data which should be compressed, aggregated, and communicated from a radar platform (or a network of radars) to a ground station through wireless links. In this paper, a new data aggregation technique is proposed towards efficient payload transmission in a network of aerial ViSAR vehicles. Our proposed method is a combination of a recent interpolation-based data hiding (IBDH) technique and visual data transformation process using discrete cosine transform (DCT) which is able to outperform the reference method in terms of data aggregation ability.
KeywordsInterpolation-based data hiding (IBDH) Video synthetic aperture radar (ViSAR) Discrete cosine transform (DCT) ViSAR sensor networks Internet of things (IoT)
Video synthetic aperture radars
Internet of things
Interpolation-based data hiding
Discrete cosine transform
Interpolation-based data hiding using discrete cosine transform
Karhunen Loeve transform
Average capacity index
Peak signal to noise ratio
Edge preservation index
Mean square error
Bit per pixel
Video synthetic aperture radar (ViSAR) is a new imaging mode of SAR to generate video sequences [1, 2]. ViSAR is recently used for aerial remote sensing imaging with air-borne radar platforms. Despite the conventional SAR sensors for capturing still images, communication data rate needed for ViSAR sensors is extremely more of which the current implemented systems mostly do not send their acquired data through wireless communication links. In fact, they have to store the data into memory and after landing, data is transferred physically to remote sensing surveillance centers to be analyzed. This shortfall is caused by two reasons; at first, frame formation process (like SAR image formation) is a relatively complicated and time-consuming procedure. Thus, while the imaging system in ViSAR mode has to generate many frames, for example 16–24 frames per second, this issue would be a big challenge. Researchers who are working on ViSAR imaging techniques have a substantial focus on this point that computational complexity must be reduced alongside improving the frame acquisition quality. In addition, using powerful computers, high-performance hardware implementation and benefits of parallel programing can speed up the formation process. The second issue that can be noted is to have a large data size for video frames (including processed frames from raw data and other related data for control and managerial information) that should be compressed and aggregated to be transferable for wireless transmission through a low-bandwidth link. Otherwise, we have to use the ViSAR technology just for non-real-time applications whereas the main idea behind ViSAR is to apply it for real-time monitoring and surveillance in remote sensing, smart cities, and civil applications in all the time and all weather (for instance, natural hazards and traffic control even in dark environment without any light source). Here, we do not work on efficient image/frame formation because it is a problem for signal processing experts to process raw data of radar sensing. Instead, we try to aggregate relevant managerial and control data and embed this data into the video frames considering specific features of SAR videos. This can reduce the data size significantly and is indeed a process towards data compression.
2 Proposed method
In order to extend the reference IBDH algorithm , we use DCT as a decomposition transform to change the error image histogram compared to the basic algorithm. In fact, we want to create a quasi-sparse frame  with less zero pixels (fully black pixels) and much more non-zero pixels which their gray levels are very near to zero. One of the most popular ways to modify interpolation-based data hiding techniques is to use a better interpolator or histogram modification through histogram shifting and histogram adjustment. As IBDH method in  is a most recent version of IBDH techniques that uses a novel interpolator alongside a histogram modification process , we wish to combine this method with another process based on discrete cosine transform (DCT) to improve its aggregation performance. In this regard, we use DCT with different patch sizes to make a combinational approach entitled interpolation-based data hiding using discrete cosine transform (IBDH-DCT). Our experiments show medium-sized patches are more effective. If a transform is able to create a quasi-sparse image with less zero pixels, it is probably able to improve IBDH in ViSAR frames. As we know, the mentioned transform can be invertible generally, but in the use of it to make transformed frames, we have to scale and quantize the coefficients matrix, so after re-scaling, a loss may be seen because of the quantization. However, this loss does not affect the watermark/embedded data, but the final data hiding approach might be non-reversible. In the next sub-sections, basic concepts around DCT will be reviewed at first, and then, the proposed method will be presented.
2.1 2D DCT for frame transformation
2.2 Quasi-sparse bit injection using IBDH and DCT
Algorithm 1: The embedding process in IBDH-DCT at the sender side.
Input: An original host frame and hidden data.
1) Compute DCT coefficients of the original host frame.
2) Scale the DCT coefficients matrix into an interval of [0,255].
3) Quantize scaled DCT coefficients matrix according to a digital image and consider as a new host frame with quasi-sparse spatial distribution.
4) Down-sample the quasi-sparse host frame (standard down-sampling is used).
5) Calculate a reconstructed version (up-scaled interpolated frame) of quasi-sparse host frame using interpolation technique.
6) Calculate an error image by subtraction of the original quasi-sparse host frame and its interpolated version considering histogram modification.
7) Calculate four key parameters of the reference IBDH technique based on histogram of the error image.
8) Inject bits of hidden data into the quasi-sparse host frame according to key parameters in the prior step and create a watermarked frame.
9) Transfer the watermarked frame to the receiver along with all key parameters computed at sender side.
Output: The watermarked frame and key parameters related to the error image.
Algorithm 2: The extraction process in IBDH-DCT at the receiver side.
Input: Receive watermarked frame, and the key parameters in Algorithm 1.
1) Extract the hidden bits and the error image through an inverse function in IBDH theory (see the main source for IBDH details).
2) Down-sample the watermarked frame (standard down-sampling is used to have a down-sampled version which is exactly equal to the down-sampled version of original frame in Algorithm 1).
3) Re-construct the down-sampled frame of the prior step by interpolator to generate the interpolated frame.
4) Restore the quasi-sparse host frame by adding error image and the interpolated frame.
5) Rescale the quasi-sparse host frame to generate approximate DCT coefficients.
6) Compute an approximate version of the rescaled quasi-sparse host frame through inverse DCT as the original host frame.
Output: The original host frame and injected bits.
Algorithm 1 includes all steps of data embedding process at the sender, and Algorithm 2 contains steps of the reverse process at the receiver side which is named extraction. The proposed method is not although fully reversible in terms of the host image reversibility because the frame transformation process is lossy; however, this transformation process is near-lossless with a loss that can be ignored. Since we use a real decomposition transform, near-lossless happens (for example in the case of FFT with complex basis, a huge loss happens). Therefore, all the process can be near-lossless. On the other hand, because there is a full reversibility for the hidden data, we can compute quality metrics in the transformed samples.
3 Results and discussion
Quality and aggregation performance in the reference method  and the proposed approaches entitled IBDH-DCT (best results are shown in italicized form)
IBDH method in 
1 × 1
2 × 2
4 × 4
8 × 8
16 × 16
32 × 32
64 × 64
128 × 128
256 × 256
Complexity analysis through execution times (best results are shown in italicized form)
Execution times (s)
Time elapsed for image transformation
Time elapsed for bit embedding process
IBDH method in 
1 × 1
2 × 2
4 × 4
8 × 8
16 × 16
32 × 32
64 × 64
128 × 128
256 × 256
The simulation results clearly show that DCT-based approach can be effective for sample frames compared to the reference method. It is noticeable that all combinational forms based on DCT decomposition are more complex than the reference method because two image transformation steps (direct + inverse) should be performed in them, in addition more time is needed to find suitable places for injecting bits because their histograms are complicated. However, this more execution time of the proposed method is a cost for having better aggregation performance. Another cost is a little loss for just host frame in combinational approaches which would be acceptable and optimized in most of real-world applications.
Table 1 shows some smaller patches cannot outperform the reference method; however, 32-by-32, 64-by-64, 128-by-128, and 256-by-256 patches have recorded the best performance in terms of similarity measures, edge handling indicator, and aggregation capacity (italicized values).
In this research, a new data aggregation method based on discrete cosine transform and quasi-sparse bit injection for IoT-enabled ViSAR sensor networks was proposed towards enhancing the embedding capacity (or aggregation performance). This method could outperform a recent data hiding approach which was used as a reference method in our work. We used four various metrics to evaluate efficiency of the proposed method in terms of general frame quality (similarity and edge handling) and aggregation performance, and finally, all of them approved its suitability. One of the findings of our research is to show the importance of checking different patch sizes. In our experiments, average-sized patches and upper-average cases were the best selections. Moreover, a study on complexity using execution times was performed which can help us find the best DCT patches. As a next idea of research, we can work on more suitable decomposition transforms to create a quasi-sparse space in order to improve the aggregation performance once again in SAR/ViSAR systems. In addition, finding a high-performance, fully lossless decomposition transform can make the aggregation mechanism reversible which may be important in some specific applications.
There are many decomposition techniques like KLT that can be used for this application, but the main focus of our research was on how to combine a state-of-the-art data hiding method with a powerful decomposition technique towards quasi-sparse bit injection. Of course, investigation on application of other transforms (instead of DCT) can be done as a future work. Specifically, KLT is not suitable for real-time processing because of an inherent high computational complexity compared to DCT. FFT is a complex transform and is not therefore suitable for this frame transformation towards quasi-sparsity. One of the good ideas can thus be wavelet. In the current version, just the process of extracting injected bits is fully reversible (lossless).
We would like to thank Sandia National Laboratory for ViSAR data used as dataset in this research.
MK participated in mathematical design of the proposed method and its computer implementation. SS coordinated industrial application and raw data preparation and helped out for study. MK and SS have completed the first draft of this paper. All authors have read and approved the final manuscript.
The authors declare that they have no competing interests.
- 1.B. Bahri-Aliabadi, M.R. Khosravi, S. Samadi, Frame Rate Computing in Video SAR Using Geometrical Analysis, The 24th Int'l Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'18), pp. 165-167, 2018; Las Vegas. USA.Google Scholar
- 2.M.R. Khosravi, S. Samadi, R. Mohseni, Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD (Medical and Radar Data, Current Signal Transduction Therapy, 2019)Google Scholar
- 5.M. Arabzadeh, H. Danyali, M. S. Helfroush, Reversible Watermarking Based on Interpolation Error Histogram Shifting, International Symposium on Telecommunications (IST'2010), pp. 840-845, 2010.Google Scholar
- 6.M.A. Carreira-Perpinán et al., Alternating optimization of decision trees, with application to learning sparse oblique trees, 32nd Conference on Neural Information Processing Systems (Montr´eal, Canada, 2018)Google Scholar
- 7.M.R. Khosravi, H. Rostami, S. Samadi, "Enhancing the Binary Watermark-Based Data Hiding Scheme Using an Interpolation-Based Approach for Optical Remote Sensing Images". International Journal of Agricultural and Environmental Information Systems 9(2), 53–71 (2018). https://doi.org/10.4018/IJAEIS.2018040104.CrossRefGoogle Scholar
- 8.M.R. Khosravi et al., A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling. Current Signal Transduction Therapy (2019)Google Scholar
- 11.S. Zhang, T. Gao, L. Yang, A reversible data hiding scheme based on histogram modification in integer DWT domain for BTC compressed images. International Journal of Network Security 18(4), 718–727 (2016)Google Scholar
- 13.A. Malik, G. Sikka, H. Verma, An image interpolation based reversible data hiding scheme using pixel value adjusting feature. Multimedia Tools and Applications (2016)Google Scholar
- 18.R.C. Gonzalez, R.E. Woods, Digital Image Processing, third edn. (Prentice Hall, NJ, 2008)Google Scholar
- 20.P. Getreuer, Zhang-Wu (Directional LMMSE Image Demosaicking, Image Processing On Line (IPOL), 2011)Google Scholar
- 22.V. Karimi, OFDM waveform design based on mutual information for cognitive radar applications. The Journal of Supercomputing (2019)Google Scholar
- 24.M. Yazdi, An Efficient Training Procedure for Viola-Jones Face Detector, International Conference on Computational Science and Computational Intelligence (ICCSCI) (Las Vegas, USA, 2017)Google Scholar
- 26.M. Singhal, Optimization of hierarchical regression model with application to optimizing multi-response regression k-ary trees, Association for the Advancement of Artificial Intelligence (AAAI) (Honolulu, Hawaii, USA, 2019)Google Scholar
- 27.M. R. Khosravi, S. Samadi, Modified Data Aggregation for Aerial ViSAR Sensor Networks in Transform Domain, 25th Int'l Conf. Par. and Dist. Proc. Tech. and Appl. (PDPTA'19), pp. 87-90, 2019.Google Scholar
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