This paper proposes an objective film grain similarity model using a data-driven approach, which aligns closely with human perception, demonstrating a high correlation with subjective studies.
Digital cinematography has advanced, yet many artists prefer film rolls for their distinctive texture and essence, with film grain being integral to their artistic expression. However, Over-the-Top (OTT) providers and streamers face challenges with this high-entropy signal, unfriendly to compression due to limited bandwidth. Preserving film grain involves removing it at the source and resynthesizing it post-decoding, a strategy supported by codecs like AV1 and VVC, albeit potentially compromising grain fidelity.
Our subjective studies, presented at IBC 2023, examined existing film grain synthesis methods, revealing shortcomings in replicating the original grain appearance. In this paper, we propose...
Exclusive Content
This article is available with a Technical Paper Pass
Dynamic streaming content packaging with C2PA
Tech Papers 2026: This paper presents an implementation of the approach adopted by C2PA for live video to dynamic packaging.
Dynamic power control for sustainable broadcast transmitter networks
Tech Papers 2026: This paper proposes an approach that uses predictive modelling in combination with real-time interference monitoring to optimise transmitter powers dynamically, with minimal impact on the consumer.
A standardised framework for C2PA provenance in media workflows
Tech Papers 2026: This paper presents the first standardised framework for implementing C2PA for media provenance across newsrooms of varying sizes and operational contexts.
Building MXL together: Progress of the multi-vendor open-source media exchange SDK
Tech Papers 2026: This paper presents the Media eXchange Layer (MXL), an open-source SDK project hosted by the Linux Foundation in collaboration with the EBU and NABA.
Search first, inspect visually when needed: A multi-agent architecture for semantic video archival retrieval
Tech Papers 2026: This paper introduces Smart Chat, a multi-agent video question-answering system that searches indexed video moments, localizes candidate evidence, and inspects a short clip only when visual verification is needed.





