Design and development of compressed video sensing technique using shuffled sailfish optimization algorithm

In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly attaining compressed video data through arbitrary projections of each frame. However, it is a major issue in generating sophisticated videos. Thi...

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Veröffentlicht in:	Signal, image and video processing Jg. 18; H. 4; S. 3537 - 3551
Hauptverfasser:	Gayathri, D., PushpaLakshmi, R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2024 Springer Nature B.V
Schlagworte:	Algorithms Blocking Coding standards Computer Imaging Computer Science Efficiency Frames (data processing) Huffman codes Hypotheses Image Processing and Computer Vision Multimedia Multimedia Information Systems Neural networks Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Signal to noise ratio Signal,Image and Speech Processing Video compression Video data Vision Wavelet transforms Compressed video sensing Decoder Space–time quantization Encoder Block partitioning
ISSN:	1863-1703, 1863-1711
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Abstract	In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly attaining compressed video data through arbitrary projections of each frame. However, it is a major issue in generating sophisticated videos. This paper devises a technique, namely a Shuffled sailfish optimizer (SSFO) for compressive video sensing using an encoder and decoder. The input video is divided into various Groups of Pictures and non-key frames. The video frames are divided into non-over-blocking blocks and every block is termed a vectorized column. The measurement vectors are quantized with Space Time Quantization and the bits linked with GOP are crowded in the packet and fed to the decoder once undergoing Huffman encoding. Once the decoder obtains the packet, it rebuilds GOP, and then the joint reconstruction is done with the proposed SSFO technique. Here, the proposed SSFO is obtained by combining the shuffled shepherd optimization algorithm, and the Sailfish optimizer. It utilizes a similar measurement matrix. The proposed SSFO outperformed with the highest Peak signal to noise ratio of 54.362 dB, Second derivative like measure of enhancement of 58.081 dB and structural similarity index measure of 0.927.
AbstractList	In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly attaining compressed video data through arbitrary projections of each frame. However, it is a major issue in generating sophisticated videos. This paper devises a technique, namely a Shuffled sailfish optimizer (SSFO) for compressive video sensing using an encoder and decoder. The input video is divided into various Groups of Pictures and non-key frames. The video frames are divided into non-over-blocking blocks and every block is termed a vectorized column. The measurement vectors are quantized with Space Time Quantization and the bits linked with GOP are crowded in the packet and fed to the decoder once undergoing Huffman encoding. Once the decoder obtains the packet, it rebuilds GOP, and then the joint reconstruction is done with the proposed SSFO technique. Here, the proposed SSFO is obtained by combining the shuffled shepherd optimization algorithm, and the Sailfish optimizer. It utilizes a similar measurement matrix. The proposed SSFO outperformed with the highest Peak signal to noise ratio of 54.362 dB, Second derivative like measure of enhancement of 58.081 dB and structural similarity index measure of 0.927. In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly attaining compressed video data through arbitrary projections of each frame. However, it is a major issue in generating sophisticated videos. This paper devises a technique, namely a Shuffled sailfish optimizer (SSFO) for compressive video sensing using an encoder and decoder. The input video is divided into various Groups of Pictures and non-key frames. The video frames are divided into non-over-blocking blocks and every block is termed a vectorized column. The measurement vectors are quantized with Space Time Quantization and the bits linked with GOP are crowded in the packet and fed to the decoder once undergoing Huffman encoding. Once the decoder obtains the packet, it rebuilds GOP, and then the joint reconstruction is done with the proposed SSFO technique. Here, the proposed SSFO is obtained by combining the shuffled shepherd optimization algorithm, and the Sailfish optimizer. It utilizes a similar measurement matrix. The proposed SSFO outperformed with the highest Peak signal to noise ratio of 54.362 dB, Second derivative like measure of enhancement of 58.081 dB and structural similarity index measure of 0.927.
Author	Gayathri, D. PushpaLakshmi, R.
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Cites_doi	10.1142/S0219691321500211 10.1007/s11042-016-3390-6 10.1155/2017/4589124 10.1109/TCSVT.2012.2207269 10.1016/j.neucom.2020.04.072 10.3390/s20010206 10.1155/2016/6950592 10.1504/IJMC.2019.102723 10.1109/5.959344 10.1109/TCSVT.2020.2978703 10.1016/j.dsp.2019.102591 10.1109/TSP.2011.2170977 10.1109/ACCESS.2019.2954140 10.1016/j.engappai.2019.01.001 10.1016/j.jvcir.2019.102734 10.1155/2012/352167 10.1155/2015/562840 10.1007/s11042-013-1743-y 10.1108/EC-10-2019-0481 10.1109/PROC.1985.13240 10.1109/PCS.2009.5167431 10.1109/TMM.2020.2975420 10.1109/ICIP.2009.5414631 10.1109/ICASSP.2009.4959797 10.1109/ICDSP.2007.4288604
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Snippet	In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly...
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SubjectTerms	Algorithms Blocking Coding standards Computer Imaging Computer Science Efficiency Frames (data processing) Huffman codes Hypotheses Image Processing and Computer Vision Multimedia Multimedia Information Systems Neural networks Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Signal to noise ratio Signal,Image and Speech Processing Video compression Video data Vision Wavelet transforms
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Title	Design and development of compressed video sensing technique using shuffled sailfish optimization algorithm
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