A survey on motion estimation and de-hazing algorithms and architectures

Motion Estimation is used many video processing algorithms for video compression, moving object detection, 3D reconstruction, video segmentation, etc. Video standards use motion estimation in the encoder and decoder to compress the video. It becomes difficult to determine motion vectors when the video is prone to weather conditions like haze. So, there is a necessity to remove haze for video analysis. In this paper, hardware and software implementation of Motion Estimation (ME) and Haze removal techniques are reviewed. Architectures like low power and parallelism Motion Estimation techniques for Block Matching algorithms (BMA) in video coding standards and Deep Learning based methods Pixel-based Methods are studied and discussed. The atmospheric model for hazy images and videos is discussed. Techniques such as single-image dehazing are analyzed. Performance metrics carried out for video analysis are also mentioned. Based on the analysis and observation the advantages and drawbacks of these techniques are addressed.