On video textures generation: A comparison between different dimensionality reduction techniques

Abstract

Video texture is a new type of medium which can provide a new video with a continuously varying stream of images from a recorded video. It is created by reordering the input video frames in a way which can be played without any visual discontinuity. Recently, a new method of generating video textures has been proposed. It first apply principal components analysis (PCA) to extract signatures or patterns from the original video sequence, and then implement an autoregressive process (AR) model to synthesize new video textures. In this paper, we extend this video texture generation method by comparing PCA with other dimensionality reduction techniques such as probabilistic principal components analysis, kernel principal components analysis, independent component analysis, local linear embedding and Isomap. According to our experiments, these approaches prevail the original approach by providing us video textures with better quality.