视频扫描格式转换芯片相关算法研究
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摘要
传统的电视系统大多使用隔行扫描方案,场频为50HZ 或者60HZ。采用这样的方案是为了降低信号的带宽,使电路更容易设计。在这样的方案下,图象的垂直分辨率较低,而且将不可避免的产生诸如线抖,大面积闪烁,边缘闪烁等可见的人为干扰。而且,当显示屏幕越大时,这些干扰就越明显。为了解决这些问题,当前的隔行扫描信号必须转换为帧频更高的逐行信号。另外,为了改善显示质量,一些图像增强技术也常常被应用于电视系统。为了实现这样的高质量的逐行电视系统(以及相关的芯片),就必须对采样率转换算法进行研究。
在本文中,首先对采样率转换理论进行了回顾,研究了一系列已有的相关算法,比如:场内插值算法, 场间插值算法, 运动自适应插值算法, 以及运动补偿算法等。文章讨论了这些算法的优点和缺点,并演示了一些相关的实验结果。文章提出了一种基于新颖的基于块匹配算法的图像运动估计算法。块匹配算法是一种经典的运动估计算法,在去隔行,视频编码上得到了广泛的应用。块匹配算法也有它自身的缺陷,这主要在于,对快匹配算法中的块的大小选择,如果块的尺寸太大,则容易产生所谓的“块效应”。如果块的尺寸太小,得到的运动场又太混乱,无法反映实际的运动。本文作者应用了模式识别的方法来改善算法的效果,对算法所得的运动场进行了聚类,使得到的运动场更加平滑,这样,块匹配算法就可以选择更小的块尺寸,得到更好的处理效果。另外,不管是块匹配算法,还是聚类算法的运算量都很大,文章又采用一种多分辨率的方法对算法进行了优化,降低了算法的复杂度。
文章把运动估计算法运用到去隔行领域,提出了一种运动补偿去隔行插值策略,其基本思想是在运动估计的基础上先求出时间插值结果,再用传统的场内插值法求出空间插值结果,两个结果的平均就得到最后的插入值。文章同时也提出了一种运动自适应的去隔行算法,这种算法对每个像素点进行运动检测,如果检测到运动,就使用空间插值,如果没有检测到运动,就使用时间插值。本文还研究了场频转换问题,即把信号的场频从50HZ 或60HZ 转换到更高的频率上,同时也研究了一些图像增强算法,例如黑电平延伸,DCTI 等等。这些算法对于改善图像效果有着显著的作用。文章对算法的实现也作了讨论,提出了纯硬件电路以及用软件结合硬件电路的两种不同的实现方案。
Current video systems employ an interlaced scan scheme with a frame rate of 50HZ or 60HZ where visually annoying artifacts, such as line flickers, large area flickers, and edge flickers, are inevitably generated and more objectionable with a larger display. To solve this problem, current video signals must be converted into deinterlaced signals that have a frame rate of 60HZ. In addition, scaling function is also required to improve display quality. So, scan rate up conversion algorithms must be employed to build a video system (and relevant chip) that will match all the requirements.
In this article, first, the theory of sample rate conversion is reviewed. A series of previous algorithms are studied, such as Intra-Field interpolation, Inter-Field interpolation, Motion-Adaptive interpolation and Motion-Compensate interpolation. Their profits and drawbacks are carefully discussed and some experimental results are demonstrated. This article proposed a novel motion estimate algorithm which is based on block-matching algorithm(BMA). The BMA is the most popular algorithm in this area, which is widely used in many applications such as deinterlacing, image coding, etc. However, BMA has its own hold-backs. The size of block is often hard to determine, if a big size is chosen, the so called “blockiness”will cause trouble, if a small size is used, the algorithm often get a erratic motion-field which is not able to express the real motion. In this article, pattern recognition method is introduced to solve the problem, the algorithm use clustering method to smooth the motion-field, so it can choose a small block in BMA. The complication of BMA and clustering algorithm is a big problem in the realization of algorithm. In this article, a hierachical method is applied to optimize the algorithm , and the efficiency of the proposed algorithm is greatly improved.
Based on the proposed motion estimate algorithm, this article bring forward a motion compensate deinterlacing algorithm. The main idea of this algorithm is to use the median of motion-estimate based interpolation and traditional intra-field interpolation as the interpolation result. This algorithm can only be realized by software with the assistance of hardware. For pure hardware realization, the author proposed a motion-adaptive deinterlacing algorithm. In this algorithm, motion-detection is performed to determine whether the current pixel is in a motive area or not. If the current pixel is moving then the algorithm use inter-field interpolation, else it
在本文中,首先对采样率转换理论进行了回顾,研究了一系列已有的相关算法,比如:场内插值算法, 场间插值算法, 运动自适应插值算法, 以及运动补偿算法等。文章讨论了这些算法的优点和缺点,并演示了一些相关的实验结果。文章提出了一种基于新颖的基于块匹配算法的图像运动估计算法。块匹配算法是一种经典的运动估计算法,在去隔行,视频编码上得到了广泛的应用。块匹配算法也有它自身的缺陷,这主要在于,对快匹配算法中的块的大小选择,如果块的尺寸太大,则容易产生所谓的“块效应”。如果块的尺寸太小,得到的运动场又太混乱,无法反映实际的运动。本文作者应用了模式识别的方法来改善算法的效果,对算法所得的运动场进行了聚类,使得到的运动场更加平滑,这样,块匹配算法就可以选择更小的块尺寸,得到更好的处理效果。另外,不管是块匹配算法,还是聚类算法的运算量都很大,文章又采用一种多分辨率的方法对算法进行了优化,降低了算法的复杂度。
文章把运动估计算法运用到去隔行领域,提出了一种运动补偿去隔行插值策略,其基本思想是在运动估计的基础上先求出时间插值结果,再用传统的场内插值法求出空间插值结果,两个结果的平均就得到最后的插入值。文章同时也提出了一种运动自适应的去隔行算法,这种算法对每个像素点进行运动检测,如果检测到运动,就使用空间插值,如果没有检测到运动,就使用时间插值。本文还研究了场频转换问题,即把信号的场频从50HZ 或60HZ 转换到更高的频率上,同时也研究了一些图像增强算法,例如黑电平延伸,DCTI 等等。这些算法对于改善图像效果有着显著的作用。文章对算法的实现也作了讨论,提出了纯硬件电路以及用软件结合硬件电路的两种不同的实现方案。
Current video systems employ an interlaced scan scheme with a frame rate of 50HZ or 60HZ where visually annoying artifacts, such as line flickers, large area flickers, and edge flickers, are inevitably generated and more objectionable with a larger display. To solve this problem, current video signals must be converted into deinterlaced signals that have a frame rate of 60HZ. In addition, scaling function is also required to improve display quality. So, scan rate up conversion algorithms must be employed to build a video system (and relevant chip) that will match all the requirements.
In this article, first, the theory of sample rate conversion is reviewed. A series of previous algorithms are studied, such as Intra-Field interpolation, Inter-Field interpolation, Motion-Adaptive interpolation and Motion-Compensate interpolation. Their profits and drawbacks are carefully discussed and some experimental results are demonstrated. This article proposed a novel motion estimate algorithm which is based on block-matching algorithm(BMA). The BMA is the most popular algorithm in this area, which is widely used in many applications such as deinterlacing, image coding, etc. However, BMA has its own hold-backs. The size of block is often hard to determine, if a big size is chosen, the so called “blockiness”will cause trouble, if a small size is used, the algorithm often get a erratic motion-field which is not able to express the real motion. In this article, pattern recognition method is introduced to solve the problem, the algorithm use clustering method to smooth the motion-field, so it can choose a small block in BMA. The complication of BMA and clustering algorithm is a big problem in the realization of algorithm. In this article, a hierachical method is applied to optimize the algorithm , and the efficiency of the proposed algorithm is greatly improved.
Based on the proposed motion estimate algorithm, this article bring forward a motion compensate deinterlacing algorithm. The main idea of this algorithm is to use the median of motion-estimate based interpolation and traditional intra-field interpolation as the interpolation result. This algorithm can only be realized by software with the assistance of hardware. For pure hardware realization, the author proposed a motion-adaptive deinterlacing algorithm. In this algorithm, motion-detection is performed to determine whether the current pixel is in a motive area or not. If the current pixel is moving then the algorithm use inter-field interpolation, else it
引文
[1] A. Murat 数字视频处理[M], 崔之枯等译. 北京:电子工业出版社1998.
[2] Chung J. Kuo, Ching Liao, and Ching C. Lin, Adaptive Interpolation Techniques for Scanning Rate Conversions[J], IEEE Trans .on Circuits and Systems for Video Technology, vol.6, No. 3, pp.317-321, June, 1996.
[3] T. Doyle, Interlaced to Sequential Conversion for EDTV Applications, [J] in proc. 2nd International Workshop Signal on Processing for HDTV, pp.412-430, Feb. 1988.
[4] Tekalp A M. Digital Video Processing[M]. Prentice Hall, 1998.
[5] K.R.Castleman,数字图像处理[M].朱志刚, 石定机等译. 北京:电子工业出版社1998.
[6] Stephan Thurnhofer, S.K.Mitra, “Edge-enhanced image zooming,”Journal of Optical Engineering, vol.35, No.7, pp.1862-1869, July, 1996.
[7] D. Van De Ville, B. Rogge, W. Philips, and I. Lemahieu, “Deinterlacing Using Fuzzy-Based Motion Detection,”Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference, 1999, pp263-267.
[8]Dang-Jiang Wang and Jin-Jang Leou, “A New Approach to Video Format Conversion Using Bidirectional Motion Estimation and Hybrid Error Concealment,”Journal of Information Science and Engineering 17, pp763-777, 2001.
[9]G. De Haan, E.B. Bellers, "Deinterlacing-an Overview", Proceedings of the IEEE, vol. 86, Issue: 9, pp 1839 -1857, Sept. 1998.
[10] G. de Haan, E.B. Bellers, "De-interlacing of Video Data ", IEEE Transactions on Consumer Electronics, Vol.43 Issue: 3, pp. 819 -825, Aug. 1997.
[11] R.J. Schutten, G. de Haan, “Real-time 2-3 Pull-down Elimination Applying Motion Estimation/Compensation in a Programmable Device”, IEEE Transactions on Consumer Electronics, Vol. 44 Issue: 3, pp. 930 –938, Aug. 1998.
[12] 章毓晋,图像工程上册北京:清华大学出版社,1999
[13] 罗希平,田捷,诸葛婴等图像分割方法综述.模式识别与人工智能,1999 12(3) 300-309
[14] 刘伟强,陈鸿,夏德深基于马尔可夫随机场的快速图像分割中国图像图形学报,2001 6(A) 228-233
[15] 柏子游,张勇,虞烈一种彩色图像的色彩分割方法模式识别与人工智能,1999 12(2) 241-244
[16] G. de Haan, and E. B. Bellers, “Deinterlacing –An Overview,”Proceedings of
[2] Chung J. Kuo, Ching Liao, and Ching C. Lin, Adaptive Interpolation Techniques for Scanning Rate Conversions[J], IEEE Trans .on Circuits and Systems for Video Technology, vol.6, No. 3, pp.317-321, June, 1996.
[3] T. Doyle, Interlaced to Sequential Conversion for EDTV Applications, [J] in proc. 2nd International Workshop Signal on Processing for HDTV, pp.412-430, Feb. 1988.
[4] Tekalp A M. Digital Video Processing[M]. Prentice Hall, 1998.
[5] K.R.Castleman,数字图像处理[M].朱志刚, 石定机等译. 北京:电子工业出版社1998.
[6] Stephan Thurnhofer, S.K.Mitra, “Edge-enhanced image zooming,”Journal of Optical Engineering, vol.35, No.7, pp.1862-1869, July, 1996.
[7] D. Van De Ville, B. Rogge, W. Philips, and I. Lemahieu, “Deinterlacing Using Fuzzy-Based Motion Detection,”Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference, 1999, pp263-267.
[8]Dang-Jiang Wang and Jin-Jang Leou, “A New Approach to Video Format Conversion Using Bidirectional Motion Estimation and Hybrid Error Concealment,”Journal of Information Science and Engineering 17, pp763-777, 2001.
[9]G. De Haan, E.B. Bellers, "Deinterlacing-an Overview", Proceedings of the IEEE, vol. 86, Issue: 9, pp 1839 -1857, Sept. 1998.
[10] G. de Haan, E.B. Bellers, "De-interlacing of Video Data ", IEEE Transactions on Consumer Electronics, Vol.43 Issue: 3, pp. 819 -825, Aug. 1997.
[11] R.J. Schutten, G. de Haan, “Real-time 2-3 Pull-down Elimination Applying Motion Estimation/Compensation in a Programmable Device”, IEEE Transactions on Consumer Electronics, Vol. 44 Issue: 3, pp. 930 –938, Aug. 1998.
[12] 章毓晋,图像工程上册北京:清华大学出版社,1999
[13] 罗希平,田捷,诸葛婴等图像分割方法综述.模式识别与人工智能,1999 12(3) 300-309
[14] 刘伟强,陈鸿,夏德深基于马尔可夫随机场的快速图像分割中国图像图形学报,2001 6(A) 228-233
[15] 柏子游,张勇,虞烈一种彩色图像的色彩分割方法模式识别与人工智能,1999 12(2) 241-244
[16] G. de Haan, and E. B. Bellers, “Deinterlacing –An Overview,”Proceedings of