In this work we present solutions based on AI techniques to the problem of real-time video quality improvement, addressing both video super resolution and compression artefact removal. These solutions can be used to revamp video archive materials allowing their reuse in modern video production and to improve the end user experience playing streaming videos in higher quality while requiring less bandwidth for their transmission. The proposed approaches can be used on a variety of devices as a post-processing step, without requiring any change in existing video encoding and transmission pipelines. Experiments on standard video datasets have shown that the proposed approaches improve video quality metrics considering either fixed bandwidth budgets or fixed quality goals.
Fast and effective AI approaches for video quality improvement
Tiberio Uricchio;
2022-01-01
Abstract
In this work we present solutions based on AI techniques to the problem of real-time video quality improvement, addressing both video super resolution and compression artefact removal. These solutions can be used to revamp video archive materials allowing their reuse in modern video production and to improve the end user experience playing streaming videos in higher quality while requiring less bandwidth for their transmission. The proposed approaches can be used on a variety of devices as a post-processing step, without requiring any change in existing video encoding and transmission pipelines. Experiments on standard video datasets have shown that the proposed approaches improve video quality metrics considering either fixed bandwidth budgets or fixed quality goals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
