根据视频区域差异智能调整锐化强度，提升画质同时节省码率。
Region-Adaptive Video Sharpening via Rate-Perception Optimization
- 基于编码树单元掩码动态分配增强比特
- 在多个基准上实现画质提升与码率降低
- 适合需要高效视频增强的系统集成场景
锐化是广泛应用的视频增强技术，但统一的锐化强度会忽略纹理差异，导致画质下降。同时，锐化会增加码率，现有方法缺乏对新增比特在不同区域的最优分配机制。为此，本文提出RPO-AdaSharp，一种端到端的区域自适应视频锐化模型，兼顾感知质量提升与码率节省。利用编码树单元（CTU）分割掩码作为先验信息，指导并约束增强比特的分配。在多个基准测试上，实验结果表明该模型在定性和定量层面均有效。
原文摘要 · Abstract (English)
Sharpening is a widely adopted video enhancement technique. However, uniform sharpening intensity ignores texture variations, degrading video quality. Sharpening also increases bitrate, and there's a lack of techniques to optimally allocate these additional bits across diverse regions. Thus, this paper proposes RPO-AdaSharp, an end-to-end region-adaptive video sharpening model for both perceptual enhancement and bitrate savings. We use the coding tree unit (CTU) partition mask as prior information to guide and constrain the allocation of increased bits. Experiments on benchmarks demonstrate the effectiveness of the proposed model qualitatively and quantitatively.
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