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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-503142 |
| Date | January 2023 |
| Creators | Granström, Hugo |
| Publisher | Uppsala universitet, Avdelningen Vi3 |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 23023 |
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