Elsevier

Displays

Volume 30, Issue 1, January 2009, Pages 27-31
Displays

Motion adaptive deblurring filter for LCD

https://doi.org/10.1016/j.displa.2008.10.002Get rights and content

Abstract

Motion blurring on liquid crystal display (LCD) was modeled by the original image convoluted with a point spread function (PSF). An intensity independent PSF was first deduced by statistical approximation. Based on the PSF, a motion adaptive deblurring filter was proposed to restore the original image. The simulation of motion blurred and deblurred natural images were presented. The results indicate that the proposed deblurring filter can significantly reduce the visible blurring artifact on LCD, which has a simple one-dimensional structure and can be integrated into other video processing algorithms, e.g. frame rate doubling, to further improve the image quality.

Introduction

Motion blurring or smearing is one of the artifacts perceived on LCD, especially for a large size panel when displaying fast moving object. Motion blurring on LCD is mainly determined by slow response time of liquid crystal (LC) and hold driving scheme which normally holds the luminance of displayed image for one frame time [1], [2]. For moving object on a screen, the eyes will smoothly follow the trajectory of the moving object by smooth pursuit eye movement [3], and integrate the luminance both in spatial and temporal domain. As a consequence, the slower the response time of LC and the longer the hold time is, the worse the perceived motion blurring is.

Many methods have been proposed to reduce the motion blurring. The first kind of method is to reduce the response time of LC, e.g. over drive scheme (OD) method [4], optically compensated bend mode LCD (OCB) method [5], and Dynamic capacitance compensation (DCC) method [6]. These methods have significantly reduced the response time, and recently less than one frame time has been achieved. As described in [2], [7], when response time of LC is less than one frame time, further reducing the response time cannot significantly improve the image quality, since then the motion blurring is mainly caused by long hold time. The second kind of method is to reduce the hold time, e.g. black data insertion method [8], scanning backlight method [9], and frame rate doubling method [10]. Reducing the hold time can significantly reduce the motion blurring artifact, but it may also bring other visible artifacts, e.g. the scanning backlight may cause flickering artifact, the frame rate doubling may bring errors and visible artifacts due to interpolation. Even when the frame rate is doubled to be 120 Hz, motion blurring is sometimes perceivable for fast moving object. The third kind of method is to compensate the luminance integration along object’s moving trajectory, e.g. motion adaptive edge compensation method [11], edge enhancement method [12], and motion compensated inverse filter method [13]. The performance of these methods depends on the tradeoff between motion blurring reduction, noise amplification in high frequency, and image content overflow (e.g. the gray scale of filtered image is outside the range that can be displayed). To improve the image quality on LCD, normally a hybrid method was implemented, e.g. motion compensation filter and frame rate doubling algorithm were integrated into one signal processing circuit on a LCD panel with fast response time of LC. A simple and low complexity motion compensation filter is one of the key algorithms for this hybrid method to further improve the image quality on LCD.

The purpose of this paper was to develop a motion adaptive deblurring filter with lower complexity. First, an intensity independent point spread function (PSF) for moving image on LCD was discussed in Section 2. Based on the PSF, a motion adaptive deblurring filter was proposed in Section 3, which had adaptive coefficients to suppress noise and prevent overflow. The simulation of motion blurred and deblurred images, based on the PSF and deblurring filter, were presented in Section 4.

Section snippets

The analysis of PSF

Motion blurred image can be modeled by convolution between the original image and a PSF. Below (1) describes the relation between the original image and the blurred image.E(x,y)=I(x,y)PSF(x,y)+N(x,y)where, I(x,y), PSF(x,y), N(x,y), E(x,y) denote the two-dimensional original image content, the point spread function, the noise, and the blurred image, respectively. To restore the original image, lots of deconvolution methods have been proposed. Depending on whether the PSF and the noise function

The deblurring filter

For the natural image, with the function of PSF, deblurring or deconvolution can be easily implemented by direct inverse method. However, as depicted in Fig. 3, the frequency response of direct inverse filter (the dotted line in Fig. 3) is not realizable. Instead, to suppress noise and avoid overflow, motion adaptive filter, which has a shape as the dashed line depicted in Fig. 3, is preferred. In [13], a width adaptive filter was proposed according to the moving speed. This filter needs to

Simulation experiment

For most study on LCD motion blurring, the discussions were always limited on simple image content with global moving speed. To compare with these discussions, we first simulated a motion blurred natural image with global moving speed. Fig. 6(a) shows the original image. We assumed that the whole image content moved from left to right with a constant speed v = 6 pixels/frame. The PSF for the whole image was deduced from (11). Fig. 6(b) shows the simulated motion blurred image. The deblurring

Discussion

As described in Eq. (1), display system may also bring noise into video content. In this paper, the deblurring was implemented on original video content, so the effect of noise was assumed not as serious as the overflow. For low resolution video content, the overflow sometimes was perceivable after deblurring, and thus small a1[n] value should be used. For most natural video content, however, the spectral power is located in low frequency [19]. With carefully selecting coefficients of

References (19)

  • Richard J. Krauzlis et al.

    Tracking with the Mind’s Eye

    Trends in Neurosciences

    (1999)
  • Yoshifumi Shimodaira

    Fundamental phenomena underlying artifacts induced by image motion and the solutions for decreasing the artifacts on FPDs

    Digest of SID

    (2003)
  • Hao Pan et al.

    Quantitative analysis of LCD motion blur and performance of existing approaches

    Digest of SID

    (2005)
  • H. Okumura et al.

    A new low-image-lag drive method for large size LCTVs

    Journal of the SID

    (1993)
  • Hirofumi Wakemoto et al.

    Advances in OCB mode LCDs improvement of moving picture quality and control of bend alignment

    Proceedings of SPIE

    (2006)
  • Seung-Woo Lee et al.

    Improved technology for motion artifact elimination in LCD monitors advanced DCC

    Digest of SID

    (2005)
  • Michiel A. Klompenhouwer

    The temporal MTF of displays and related video signal processing

    IEEE International Conference on Image Processing

    (2005)
  • Sunkwang Hong et al.

    Enhancement of motion image quality in LCD

    Digest of SID

    (2004)
  • A.A.S. Sluyterman et al.

    Architecture choices in a scanning backlight for large LCD TVs

    Digest of SID

    (2005)
There are more references available in the full text version of this article.

Cited by (0)

View full text