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    "abstract": "Film grain is a distinctive characteristic of analog film that contributes to the cinematic look of videos but reduces compression efficiency, making video distribution challenging-especially at low bitrates. AV1 film grain synthesis framework addresses this by separating and estimating film grain during encoding and reconstructing it during decoding. Film grain block size, as an important parameter of the framework, significantly impacts reconstruction quality, with larger sizes potentially causing noticeable repetitive patterns, degrading the viewing experience. In response, we propose a novel, first-of-its-kind perceptual quality assessment method to detect and penalize repetitive patterns efficiently, leveraging the characteristics of the human visual system. Our method normalizes the input frame, identifies its local similarities with grain templates to localize repetitive patterns, and utilizes perceptual pooling to score film grain pattern quality. We validate its effectiveness through a subjective experiment with expert viewers and further discuss its practical value for guiding improvements in AV1 film grain synthesis.",
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    "docLabel": "MIJ 2025, Volume 134, Number 5 (pp. 67 to 75)",
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