Relational Future Captioning Model For Explaining Likely Collisions in Daily Tasks

VMAF is a popular objective quality metric used for video quality evaluation. The power of VMAF has been demonstrated for a wide variety of video scales and encoding processes. However, its ability to evaluate the quality of small video patches has not yet been tested, despite its importance for encoding algorithms. We applied Maximum Likelihood Difference Scaling (MLDS) methodology to estimate supra-threshold perceptual differences in localized sections in videos, also known as tubes, encoded using AV1. We further used the results to assess the performance of VMAF in this scenario and proposed a recalibration of the algorithm to improve its agreement with the subjective data.