基于视频场景分析的改进运动矢量估计视频错误隐藏方法

Q3 Energy
S. M. Zabihi, Hossein Ghanei-Yakhdan, N. Mehrshad
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引用次数: 0

摘要

为了提高运动矢量估计的精度,同时减少估计过程中的误差传播问题,本文提出了一种基于视频场景信息提取的自适应误差隐藏方法。为此,首先分析降级MB周围视频场景的运动信息,估计降级MB的运动类型。如果相邻MB具有均匀运动,则降级MB通过选择并置MB的MV来模仿相邻MB的行为。否则,通过第二种提出的EC技术(即IOBMA)估计丢失的MV。在IOBMA中,与传统的基于边界匹配准则的EC技术不同,它不仅根据边界像素的亮度和色度分量评估每个边界畸变,而且计算每个候选MV对应的总边界畸变作为可用边界畸变的加权平均值。仿真结果表明,该方法在客观质量评价和主观质量评价两方面都具有较好的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Motion Vector Estimation Approach for Video Error Concealment Based on the Video Scene Analysis
In order to enhance the accuracy of the motion vector (MV) estimation and also reduce the error propagation issue during the estimation, in this paper, a new adaptive error concealment (EC) approach is proposed based on the information extracted from the video scene. In this regard, the motion information of the video scene around the degraded MB is first analyzed to estimate the motion type of the degraded MB. If the neighboring MBs possess uniform motion, the degraded MB imitates the behavior of neighboring MBs by choosing the MV of the collocated MB. Otherwise, the lost MV is estimated through the second proposed EC technique (i.e., IOBMA). In the IOBMA, unlike the conventional boundary matching criterion-based EC techniques, not only each boundary distortion is evaluated regarding both the luminance and the chrominance components of the boundary pixels, but also the total boundary distortion corresponding to each candidate MV is calculated as the weighted average of the available boundary distortions. Compared with the state-of-the-art EC techniques, the simulation results indicate the superiority of the proposed EC approach in terms of both the objective and subjective quality assessments.
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来源期刊
Iranian Journal of Electrical and Electronic Engineering
Iranian Journal of Electrical and Electronic Engineering Engineering-Electrical and Electronic Engineering
CiteScore
1.70
自引率
0.00%
发文量
13
审稿时长
12 weeks
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