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Digital Image and Video Processing Using Subpixel Rendering.

机译:使用亚像素渲染的数字图像和视频处理。

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摘要

Subpixel rendering techniques originate from the problem of monochromatic font rendering on LCDs. It takes advantage of the fact that a single pixel on a color LCD display consists of several primary colors, typically three colored stripes (subpixels) ordered red, green, and blue (RGB). Researchers found that, by controlling the subpixel values of neighboring pixels, it is possible to micro-shift the apparent position of a line to gives greater details of text.;In this thesis, we address the problem of color image and video processing using subpixel rendering techniques to achieve superior sharpness for small LCD displays by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness.;First, we discuss subpixel rendering for down-sampling problem, such a problem exists when a high resolution image or video is to be displayed on low resolution display terminals (i.e., Mobile). We start by formulating subpixel-based down-sampling as different optimization problems based on different reconstruction models in spatial domain: MMDE (Min-Max Directional Error) and MMSESD (MMSE for subpixel-based down-sampling). Simulation results show our proposed subpixel-based methods can give much sharper images compared with the conventional pixel-based methods, without noticeable color fringing artifacts.;To better understand what happens in subpixel-based algorithm, we further propose novel frequency domain analysis approach to explain why it is possible to achieve a higher apparent resolution using subpixel techniques. Our theoretical analysis shows that the cut-off frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using novel anti-aliasing filters.;We also investigate the problem of low bit-rate JPEG compression. Since JPEG introduces severe blocking artifacts under low bit-rate, researchers have proposed a down-sampled image when compressed and later interpolated scheme, which provides superior performance than high resolution image compressed directly. However, pixel-based down-sampling loses high frequency details, resulting in blurring reconstructed image. We thus proposed subpixel-based low bit-rate JPEG compression algorithm without changing decoding system, which can provide about 3dB higher PSNR than pixel-based low bit-rate JPEG compression scheme, under the same bit-rates.;Finally, we address the problem of displaying high resolution one-color Bayer image on low-resolution LCD screen of portable devices, which requires demosaicking followed by down-sampling. However, these two steps require high computational complexity, and the color artifacts introduced in demosaicking will be magnified in down-sampling. We thus propose joint demosaicking and subpixel-based down-sampling algorithm by directly performing subpixel-based down-sampling in Bayer domain without demosaicking, resulting in greatly reduced computational complexity and sharper down-sampled images.
机译:亚像素渲染技术源自LCD上单色字体渲染的问题。它利用了以下事实:彩色LCD显示器上的单个像素由几种原色组成,通常是三个彩色条纹(子像素),其顺序是红色,绿色和蓝色(RGB)。研究人员发现,通过控制相邻像素的子像素值,可以对行的表观位置进行微位移以提供更多的文本细节。在本文中,我们解决了使用子像素进行彩色图像和视频处理的问题通过控制单个子像素而非像素来实现小型LCD显示器的出色清晰度的显像技术。然而,增加的亮度分辨率通常以色度失真为代价。一个主要的挑战是在保持清晰度的同时抑制彩色边缘伪影。首先,我们讨论用于下采样问题的子像素渲染,当高分辨率图像或视频要在低分辨率显示终端上显示时(例如,移动)。我们首先将基于亚像素的下采样公式化为基于空间域中不同的重建模型的不同优化问题:MMDE(最小-最大方向误差)和MMSESD(针对基于亚像素的下采样的MMSE)。仿真结果表明,与传统的基于像素的方法相比,我们提出的基于亚像素的方法可以提供更清晰的图像,而没有明显的色边伪像。为了更好地了解基于亚像素的算法中发生的情况,我们进一步提出了一种新颖的频域分析方法解释为什么使用子像素技术可以实现更高的视在分辨率。我们的理论分析表明,使用新型抗混叠滤波器可以有效地将基于亚像素的抽取的低通滤波器的截止频率扩展到Nyquist频率以外。我们还研究了低比特率JPEG压缩的问题。由于JPEG在低比特率下引入了严重的块状伪像,因此研究人员提出了压缩后的下采样图像和后来的插值方案,与直接压缩的高分辨率图像相比,它具有更高的性能。但是,基于像素的下采样会丢失高频细节,从而导致重建图像模糊。因此,我们提出了基于子像素的低比特率JPEG压缩算法而无需更改解码系统,在相同的比特率下,该算法可以提供比基于像素的低比特率JPEG压缩方案高约3dB的PSNR。在便携式设备的低分辨率LCD屏幕上显示高分辨率的单色拜耳图像的问题,这需要去马赛克,然后进行下采样。但是,这两个步骤都需要很高的计算复杂度,并且在去马赛克处理中引入的彩色伪像会在下采样中放大。因此,我们通过在Bayer域中直接执行基于子像素的下采样而不进行去马赛克,提出了联合去马赛克和基于子像素的下采样算法,从而大大降低了计算复杂度并获得了更清晰的下采样图像。

著录项

  • 作者

    Fang, Lu.;

  • 作者单位

    Hong Kong University of Science and Technology (Hong Kong).;

  • 授予单位 Hong Kong University of Science and Technology (Hong Kong).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 145 p.
  • 总页数 145
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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