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fmetrics is an open-source library in C and Zig for computing fast perceptual image and video fidelity metrics such as IW-SSIM, MS-SSIM, SSIMULACRA2, Butteraugli, and CVVDP, with validated correlation to human ratings.
This paper proposes a model-based approach to assess massively multilingual parallel data by decomposing it into parallelism assessment and reference-free quality estimation, finding that no single universal metric works across all language directions.
This paper introduces a knowledge-based approach using knowledge graph embeddings to automatically assess big data quality by predicting missing edges between context representations and quality rules, outperforming traditional matching methods.