Evaluation of Automatically Generated Video Captions Using Vision and Language Models

intra prediction is an important technique to improve coding efficiency by exploiting the spatial redundancy present in typical video sequences. in video coding standards such as H.264/AVC, HEVC and VVC, directional predictors are utilized to generate prediction along a single direction within a block to be coded. However, these predictors fail to generate an accurate prediction when the block contains complex patterns such as periodic textures. in this paper, we propose a graph-based inpainting method that can handle both regular and near-regular textures. The proposed inpainting method utilizes a total variation model associated with the Laplacian matrix of a graph, whose edge weights are a function of pixel patch distance. We evaluate the performance of our proposed method as an additional prediction mode combined with the H.264/AVC coding standard. Experimental results show that the proposed method can significantly outperform H.264/AVC predictors in areas with high frequency periodic patterns.