论文标题
在量化域中使用弯曲的马尔可夫高斯噪声自适应抖动，以将SDR映射到HDR图像
Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image
论文作者
论文摘要
高动态范围（HDR）成像由于其逼真的内容，不仅是常规显示，而且还增加了智能手机，因此引起了人们的关注。在分发足够的HDR内容之前，HDR可视化仍然主要依赖于转换标准动态范围（SDR）内容。 SDR图像通常在SDR到HDR转换之前进行量化或位深度降低，例如用于视频传输。量化很容易导致绑带人工制品。在某些计算和/或内存I/O有限的环境中，使用空间邻域信息的传统解决方案是不可行的。我们的方法包括噪声（离线）和噪声注入（在线），并在量化图像的像素上运行。我们根据量化像素的Luma和反色调映射函数的斜率自适应地改变了噪声模式的大小和结构。主观用户评估证实了我们技术的卓越性能。
High Dynamic Range (HDR) imaging is gaining increased attention due to its realistic content, for not only regular displays but also smartphones. Before sufficient HDR content is distributed, HDR visualization still relies mostly on converting Standard Dynamic Range (SDR) content. SDR images are often quantized, or bit depth reduced, before SDR-to-HDR conversion, e.g. for video transmission. Quantization can easily lead to banding artefacts. In some computing and/or memory I/O limited environment, the traditional solution using spatial neighborhood information is not feasible. Our method includes noise generation (offline) and noise injection (online), and operates on pixels of the quantized image. We vary the magnitude and structure of the noise pattern adaptively based on the luma of the quantized pixel and the slope of the inverse-tone mapping function. Subjective user evaluations confirm the superior performance of our technique.
