In video compression procedure, movement estimation is one of the key segments due to its high computation unpredictability in finding the movement vectors between the frames. The purpose of movement estimation is to diminish the storage space, data transfer capacity, and transmission cost for transmission of video in numerous mixed media administration applications by decreasing the redundancies while preserving the better quality of the video. Each algorithm has its own benefits and culpabilities. Among these, block-based movement estimation calculations are most powerful and adaptable. In this paper, diamond search-hybrid teaching and learning-based optimization (DS-HTLBO) has been proposed for motion estimation. The performance of the proposed DS-HTLBO method is analyzed by considering different performance evaluation parameters such as peak signal-to-noise ratio, mean square error, and compression ratio. The comparative outcomes reveal that the proposed DS-HTLBO method outperformed in terms of PSNR of 41% and CR of 5.47% with other DS, 4SS, and NTSS methods.