Video compression using improved diamond search hybrid teaching and learning-based optimization model

B. Veerasamy, B. Bharathi, A. Ahilan
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Abstract

ABSTRACT Video compression is necessary to recreate a video without sacrificing quality. Nowadays, researchers are focusing on global optimization approaches to determine the optical flow of the neighboring pixels in video processing. In this work, a novel improved diamond search-hybrid teaching-learning based optimization (IDS-HTLBO) methodology has been proposed to compress the videos and increase the video quality. This method uses a diamond search pattern with a secure number of search points for per frame of the video. The hybridization of DS algorithm and TLBO algorithm are applied in this methodology to reduce computational complexity. Moreover, this method reduces the computational unpredictability of block matching. The quality of the image was validated with 3D reconstruction by the structured light approaches. The experimental result shows that the proposed IDS-HTLBO algorithm achieves a maximum average value of 53.17 dB, 0.44 and 11.57 in terms of peak-to-signal-noise ratio, mean squared error, and compression ratio respectively.
基于改进菱形搜索的视频压缩混合教与学优化模型
视频压缩是在不牺牲质量的情况下重建视频的必要条件。目前,研究人员正在研究全局优化方法来确定视频处理中相邻像素的光流。本文提出了一种改进的基于菱形搜索-混合教学的优化方法(IDS-HTLBO)来压缩视频,提高视频质量。该方法使用菱形搜索模式,为视频的每帧提供安全数量的搜索点。该方法采用了DS算法和TLBO算法的混合,降低了计算复杂度。此外,该方法降低了块匹配的计算不可预测性。通过结构光方法进行三维重建,验证了图像的质量。实验结果表明,IDS-HTLBO算法的峰值信噪比、均方误差和压缩比的最大平均值分别为53.17 dB、0.44和11.57。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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