IEEE Trans Image Process. 2018 Oct;27(10):4860-4872. doi: 10.1109/TIP.2018.2803306.
Display devices at bit depth of 10 or higher have been mature but the mainstream media source is still at bit depth of eight. To accommodate the gap, the most economic solution is to render source at low bit depth for high bit-depth display, which is essentially the procedure of de-quantization. Traditional methods, such as zero-padding or bit replication, introduce annoying false contour artifacts. To better estimate the least-significant bits, later works use filtering or interpolation approaches, which exploit only limited neighbor information, cannot thoroughly remove the false contours. In this paper, we propose a novel intensity potential (IP) field to model the complicated relationships among pixels. The potential value decreases as the spatial distance to the field source increases and the potentials from different field sources are additive. Based on the proposed IP field, an adaptive de-quantization procedure is then proposed to convert low-bit-depth images to high-bit-depth ones. To the best of our knowledge, this is the first attempt to apply potential field for natural images. The proposed potential field preserves local consistency and models the complicated contexts well. Extensive experiments on natural, synthetic, and high-dynamic range image data sets validate the efficiency of the proposed IP field. Significant improvements have been achieved over the state-of-the-art methods on both the peak signal-to-noise ratio and the structural similarity.
显示设备的位深达到 10 位或更高已经很成熟，但主流媒体源仍处于 8 位。为了适应这种差距，最经济的解决方案是为高比特深度显示渲染低比特深度的源，这实质上是去量化的过程。传统方法，如零填充或位复制，会引入令人讨厌的虚假轮廓伪像。为了更好地估计最不重要的位，后来的工作使用滤波或插值方法，这些方法只利用有限的邻域信息，不能彻底消除虚假轮廓。在本文中，我们提出了一种新的强度势（IP）场来模拟像素之间复杂的关系。势值随与场源的空间距离的增加而减小，并且来自不同场源的势是可加的。基于所提出的 IP 场，然后提出了一种自适应去量化过程，将低比特深度图像转换为高比特深度图像。据我们所知，这是首次尝试将势场应用于自然图像。所提出的势场保留了局部一致性，并很好地模拟了复杂的上下文。在自然、合成和高动态范围图像数据集上的广泛实验验证了所提出的 IP 场的有效性。在峰值信噪比和结构相似性方面，与最先进的方法相比，都取得了显著的改进。
