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arXiv AI By Emanuele Artioli, Farzad Tashtarian, Christian Timmerer

Perceptual Refinement of an End-to-End Video Streaming Pipeline via Generative AI Layers

Read the original on arXiv AI →

The paper introduces PRESLEY, an end‑to‑end video streaming pipeline that uses generative AI layers to selectively degrade and restore less important regions of a frame. By replacing destructive block removal with adaptive in‑place degradation and signaling block strength via a side channel, PRESLEY achieves significant bitrate savings and improved background quality compared to its predecessor and pristine baselines. The authors also analyze the theoretical headroom of this architecture, quantifying remaining cost‑axis headroom and modeling post‑restoration damage to guide future rate‑distortion‑restoration selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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