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. 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.