A dynamic saliency based method for video retargeting

With increased usage of smartphones, tablets and small displays to play multimedia content, video retargeting becomes an important tool for better user experience. In this thesis, we propose a novel content-based approach for video retargeting that relies on spatio-temporal saliency to estimate relevant information in videos. Our method preserves spatial saliency as well as temporal coherence. We also propose a spatio-temporal saliency algorithm designed for this application domain that combines spatial saliency with motion trajectories. We demonstrate the quality of the proposed approach through quantitative and qualitative evaluation, contrasting it with five different video retargeting methods. Quantitative evaluation is done using generic image/video quality metrics, so that they can be applied on any video retargeting solution. We have extracted the correlation between the quantitative and qualitative evaluation, to propose a new metric that is a combination of the existing quantitative metrics. The proposed metric is proven to be the best approximation to the qualitative results, thus can be used as a benchmark to evaluate video retargeting methods.