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Three-dimensional dictionary-based deblocking for high-quality stereoscopic view generation

机译:基于三维字典的解块,可生成高质量的立体视图

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摘要

Image quality and depth perception rates in three-dimensional television (3-D TV) are undesirably decreased by coding due to the loss of high-frequency components caused by a block-based discrete cosine transform transform. Representative coding artifacts are blocking artifacts that seriously degrade the picture quality and depth perception rates. We propose a new blocking artifact reduction method in 3-D TV using an overcomplete 3-D dictionary. We first generate the overcomplete 3-D dictionary from natural and depth images using the k-singular value decomposition algorithm. Then, we perform deblocking using the 3-D dictionary after estimating an error threshold of the objective function by the third-order polynomial fitting. Experimental results demonstrate that the proposed method can effectively reduce annoying blocking artifacts in compressed 3-D images, i.e., video-plus-depth, and generate high-quality 3-D stereoscopic views.
机译:由于基于块的离散余弦变换变换引起的高频分量的损失,通过编码不期望地降低了三维电视(3-D TV)中的图像质量和深度感知率。代表性的编码伪像是阻止伪像,这些伪像会严重降低图片质量和深度感知率。我们提出了一种使用超完备的3-D词典的3-D电视中新的减少块效应的方法。我们首先使用k奇异值分解算法从自然图像和深度图像生成过完整的3-D词典。然后,在通过三阶多项式拟合估计目标函数的误差阈值之后,我们使用3-D字典执行解块。实验结果表明,提出的方法可以有效地减少压缩的3D图像中烦人的块状伪像,即视频加深度,并生成高质量的3D立体视图。

著录项

  • 来源
    《Journal of electronic imaging》 |2013年第4期|043023.1-043023.13|共13页
  • 作者单位

    Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071, China;

    Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071, China;

    Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071, China;

    Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 01:17:35

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