Applying Objective Quality Metrics to Video-Codec Comparisons: Choosing the Best Metric for Subjective Quality Estimation

Авторы: Antsiferova Anastasia, Yakovenko Alexander, Safonov Nickolay, Kulikov Dmitriy, Gushin Alexander, Vatolin Dmitriy
Сборник: Proceedings of the 31th International Conference on Computer Graphics and Machine Vision
Серия: CEUR Workshop Proceedings
Том: 3027
Год издания: 2021
DOI: 10.20948/graphicon-2021-3027-199-210
Аннотация: Quality assessment is essential to creating and comparing video compression algorithms. Despite the development of many new quality-assessment methods, well-known and generally accepted codecs comparisons mainly employ classical methods such as PSNR, SSIM, and VMAF. These methods have different variations: temporal pooling techniques, color-component summations and versions. In this paper, we present comparison results for generally accepted video-quality metrics to determine which ones are most relevant to video codecs comparisons. For evaluation we used videos compressed by codecs of different standards at three bitrates, and subjective scores were collected for these videos. Evaluation dataset consists of 789 encoded streams and 320294 subjective scores. VMAF calculated for all Y, U, V color spaced showed the best correlation with subjective quality, and we also showed that the usage of smaller weighting coefficients for U and V components leads to a better correlation with subjective quality.
Добавил в систему: Анциферова Анастасия Всеволодовна

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