Greater compression ratio can be achieved while compressing images and videos by using the technique called compressive sensing or compressed sensing (CS). In CS, sparsity of the signal is exploited in order to achieve high compression. CS-based compression converts the images and video frames to a set of \(m<n\) measurements, which are transmitted to the receiver where the images and video frames are recovered by an efficient reconstruction algorithm with minimum error. Many reconstruction procedures solve least squares problem iteratively to recover the original signal, which ultimately increases the algorithmic complexity and runtime. In this work, split, process and merge technique for image and video reconstruction is proposed, which surpasses the adversities of the iterative algorithms, and provides better reconstruction performance in terms of perception and objective measures. Even with small number of measurements, the peak to signal noise ratio obtained using split and merge technique is above 30 dB, and the structural similarity is above 97% with an accuracy of 98% for both images and videos. The total runtime of the proposed algorithm is around 1.5 s and 9 s for images and videos respectively, which is small compared to iterative algorithms. Thus, the proposed split and merge technique based compressive sensing recovery algorithm proves to perform better in terms of speed and reconstruction quality.

通过使用称为压缩感测或压缩感测（CS）的技术压缩图像和视频时，可以实现更高的压缩率。在CS中，利用信号的稀疏性来实现高压缩率。基于CS的压缩将图像和视频帧转换为一组\（m <n \）测量值被发送到接收器，在接收器中通过有效的重建算法以最小的误差恢复图像和视频帧。许多重建程序迭代地解决最小二乘问题以恢复原始信号，这最终增加了算法复杂度和运行时间。在这项工作中，提出了用于图像和视频重建的分割，处理和合并技术，该技术克服了迭代算法的缺点，并在感知和客观度量方面提供了更好的重建性能。即使进行少量测量，使用拆分和合并技术获得的峰值信噪比仍高于30 dB，并且结构相似度仍高于97％，图像和视频的准确度均为98％。对于图像和视频，所提出算法的总运行时间分别约为1.5 s和9 s，与迭代算法相比很小。因此，所提出的基于分离和合并技术的压缩感知恢复算法在速度和重建质量方面被证明具有更好的性能。