Objective measurement of video quality

Abstract: Automatic video quality assessment has many potential use cases in today’s video-filledsociety, for example, when trying to find highlights in a video. This thesis studies the possibilityof extracting the best segments from a video automatically based on five selected metrics:sharpness, colorfulness, contrast, stability, and aesthetics. Multiple different methods from eachmetric category were compared against each other using datasets with subjective ratings ofimages from KADID-10k and videos from LIVE-Qualcomm. The best method from eachcategory was combined into a single video quality score. The combination was done through aweighted sum, obtained from a least-square fit on the subjective scores of a training dataset(KonViD-1k). The segment of a video with the highest average quality score was chosen as thehighlight. The video quality score achieved Spearman correlations of 0.67 and 0.7 whenevaluated on two validation datasets (KonViD-150k-B and LIVE-VQC). In conclusion, themetrics work well, but are currently too slow for the intended target platform (mobile devices)and thus future work should focus on improving their performance.