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.

中文翻译：

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在量化域中使用弯曲马尔可夫-高斯噪声的自适应抖动将 SDR 映射到 HDR 图像

高动态范围 (HDR) 成像因其逼真的内容而受到越来越多的关注，不仅适用于常规显示器，还适用于智能手机。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。量化很容易导致条带伪影。在某些计算和/或内存 I/O 受限的环境中，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生成（离线）和噪声注入（在线），并对量化图像的像素进行操作。我们根据量化像素的亮度和反色调映射函数的斜率自适应地改变噪声模式的幅度和结构。主观用户评估证实了我们技术的卓越性能。