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Current Signal Transduction Therapy

Author(s): Mohammad R. Khosravi*, Sadegh Samadi and Reza Mohseni

DOI: 10.2174/2213275912666190618165125

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Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD, Medical and Radar Data

Page: [144 - 196] Pages: 53

  • * (Excluding Mailing and Handling)

Abstract

Background: Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14.

Materials and Methods: In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample.

Results: Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem.

Conclusion: Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.

Keywords: Image interpolation, intra-frame video compression, quality assessment, coded video, computational complexity, high frequency.

Graphical Abstract

[1]
Zhefei Y. Multi-level video frame interpolation: Exploiting the interaction among different levels. IEEE Trans Circ Syst Video Tech 2013; 23: 1235-48.
[http://dx.doi.org/10.1109/TCSVT.2013.2242631]
[2]
Gonzalez RC, Woods RE. Digital image processing 3rd ed. 2008; 1-797.
[3]
Khosravi MR, Yazdi M. A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram computation and quartered interpolation with greedy weights. Neural Comput Appl 2018; 30: 2017-28.
[http://dx.doi.org/10.1007/s00521-018-3489-y]
[4]
Khosravi MR, Keshavarz A, Rostami H, Mansouri S. Statistical image fusion for HR band colorization in landsat sensors. 20th Annual Conference of Computer Society of Iran. 245-50.
[5]
Phamila Y, Amutha R. Discrete cosine transform based fusion of multi-focus images for visual sensor networks. Signal Process 2014; 95: 161-70.
[http://dx.doi.org/10.1016/j.sigpro.2013.09.001]
[6]
Chunming H, Huadong G, Changlin W. Edge preservation evaluation of digital speckle filters. IEEE International Geoscience and Remote Sensing Symposium. 4: 2471-3.
[7]
Khosravi MR. A tutorial and performance analysis on ENVI Tools for SAR image DE speckling. Curr Signal Transduct Ther 2018; 15(2): 216-23.
[http://dx.doi.org/10.2174/1574362413666181005101315]
[8]
Bahri-Aliabadi B, Khosravi MR, Samadi S. Frame rate computing in video sr using geometrical analysis. 24th Int Conf on Parallel and Distribute Process Techn and Appl (PDPTA’18). 165-7.
[9]
Team SEH. video converter software (#1 Video Converter Pro (v5228)) 2018. 2018.http://wwwvideotoxcom
[10]
Khan I, Ansari MA, Saeed SH, Khan K. Evaluation and analysis of rate control methods for H.264/AVC and MPEG-4 video codec. J Electr Comput Eng 2018; 8: 1286-94.
[11]
Kok CW, Tam WS. Digital image interpolation in MATLAB. John Wiley & Sons 2019.
[http://dx.doi.org/10.1002/9781119119623]
[12]
Keys RG. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 1981; 29(6): 1153-60.
[http://dx.doi.org/10.1109/TASSP.1981.1163711]
[13]
Hou H, Andrews H. cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 1978; 26(6): 508-17.
[http://dx.doi.org/10.1109/TASSP.1978.1163154]
[14]
Parker JA, Kenyon RV, Troxel DE. Comparison of interpolating methods for image resampling. IEEE Transact on Med Imag 1983; 2(1): 31-9.
[15]
Getreuer P. Linear methods for image interpolation. Image Process Online 2011; 1: 238-59.
[16]
Zhang L, Wu X. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Transact on Image Process 2006; 15(8): 2226-38.
[http://dx.doi.org/10.1109/TIP.2006.877407] [PMID: 16900678]
[17]
Getreuer P. Zhang-Wu. Directional LMMSE image demo sacking. Image Process Online 2011; 1: 1-10.
[18]
Luo L, Chen Z, Chen M, Zeng X, Xiong Z. Reversible image watermarking using interpolation technique. IEEE Transact on Infor Forensics Sec 2010; 5(1): 187-93.
[http://dx.doi.org/10.1109/TIFS.2009.2035975]]