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Subpixel-Based Image Scaling for Grid-like Subpixel Arrangements: A Generalized Continuous-Domain Analysis Model

机译:网格状子像素排列的基于子像素的图像缩放:广义连续域分析模型

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Subpixel-based image scaling can improve the apparent resolution of displayed images by controlling individual subpixels rather than whole pixels. However, improved luminance resolution brings chrominance distortion, making it crucial to suppress color error while maintaining sharpness. Moreover, it is challenging to develop a scheme that is applicable for various subpixel arrangements and for arbitrary scaling factors. In this paper, we address the aforementioned issues by proposing a generalized continuous-domain analysis model, which considers the low-pass nature of the human visual system (HVS). Specifically, given a discrete image and a grid-like subpixel arrangement, the signal perceived by the HVS is modeled as a 2D continuous image. Minimizing the difference between the perceived image and the continuous target image leads to the proposed scheme, which we call continuous-domain analysis for subpixel-based scaling (CASS). To eliminate the ringing artifacts caused by the ideal low-pass filtering in CASS, we propose an improved scheme, which we call CASS with Laplacian-of-Gaussian filtering. Experiments show that the proposed methods provide sharp images with negligible color fringing artifacts. Our methods are comparable with the state-of-the-art methods when applied on the RGB stripe arrangement, and outperform existing methods when applied on other subpixel arrangements.
机译:通过控制单个子像素而不是整个像素,基于子像素的图像缩放可以改善显示图像的外观分辨率。但是,提高的亮度分辨率会导致色度失真,因此在保持清晰度的同时抑制色差至关重要。此外,开发适用于各种子像素布置和任意缩放因子的方案具有挑战性。在本文中,我们通过提出广义连续域分析模型来解决上述问题,该模型考虑了人类视觉系统(HVS)的低通特性。具体地,给定离散图像和网格状子像素布置,由HVS感知的信号被建模为2D连续图像。最小化感知图像和连续目标图像之间的差异导致了提出的方案,我们将其称为基于子像素缩放(CASS)的连续域分析。为了消除由CASS中理想的低通滤波引起的振铃失真,我们提出了一种改进的方案,我们将其称为具有Laplacian-of-Gaussian滤波的CASS。实验表明,所提出的方法可提供清晰的图像,且色边伪影可忽略不计。当应用于RGB条纹排列时,我们的方法可与最新方法相媲美,而当应用于其他子像素排列时,我们的方法则优于现有方法。

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