Global bi-directional motion compensation frame interpolation algorithm

A frame interpolation algorithm for the application of low-bit-rate video coding is proposed in this paper. The global motion vectors are first estimated by computing global sum of absolute differences between frames. Then the block motion estimation is carried out according to the modified weighted correlation index criteria. The estimation is further adjusted around the moving object based on the modified weighted correlation index criteria but with a smaller factor k so that the global motion estimation is less influential to the movement vector estimation for the moving objects. Finally the motion estimation outliers are determined by the Box-and-Whisker analysis, and removed by two methods. Method 1 redoes the bi-directional estimation with a larger factor k; Method 2 computes the movement vectors of the outliers using neighboring movement vectors. The experiments showed that the proposed method can improve the peak signal to noise ratio for four test video sequences. Visual inspection also shows better frame interpolation quality.     

Visual object retrieval via block-based visual-pattern matching

This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detector. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by visual-pattern detection to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.     

Fast Inter Mode Decision Using Spatial Property of Motion Field

Variable size motion estimation with multiple reference frames has been adopted by the new video coding standard H.264. It can achieve significant coding efficiency compared to coding a macroblock (MB) in regular size with single reference frame. On the other hand, it causes high computational complexity of motion estimation at the encoder. Rate distortion optimized (RDO) decision is one powerful method to choose the best coding mode among all combinations of block sizes and reference frames, but it requires extremely high computation. In this paper, a fast inter mode decision is proposed to decide best prediction mode utilizing the spatial continuity of motion field, which is generated by motion vectors from 4 $, times ,$4 motion estimation. Motion continuity of each MB is decided based on the motion edge map detected by the Sobel operator. Based on the motion continuity of a MB, only a small number of block sizes are selected in motion estimation and RDO computation process. Simulation results show that our algorithm can save more than 50% computational complexity, with negligible loss of coding efficiency.      

基于掩模金字塔的高精度全局运动估计算法

在视频序列的全局运动估计中,前景运动对象的存在常常会大幅度地降低估计的精度,为此提出一种对前景对象自适应的高精度全局运动估计算法.该算法以像素块为单位,利用块内外点的比重判定前景区域,同时引入马尔可夫聚类方法进行后处理,有效地提高了运动对象的定位精度;通过对目标函数引入权重系数增强对残差的鲁棒性,以进一步提高算法的估计精度.此外,基于像素掩模的3层金字塔构建序列图像,并将改进的梯度方法引入到优化过程中,提高了算法的实时性.对不同运动类型的标准视频序列的实验结果表明,该算法有效地提高了全局运动估计的精度和速度.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

符合H.264标准的一种基于内容的变尺寸宏块分割方法

提出了一种基于视频内容预分析的变尺寸宏块分割(CBVSBM)的实现方案,变尺寸宏块分割是H.264编码器端提供的重要编码工具。该方案根据每帧视频的内容,自适应地在自顶向下的变尺寸运动估计(TDVSBM)和自底向上的变尺寸运动估计(BUVSBM)两种变尺寸宏块分割算法(VSBM)间进行切换,从而结合了两种算法的优点。为了实现两种VSBM算法间的自适应选择,提出了一种简单而有效的基于STGS图的预判准则。理论分析和实验结果表明,该算法的运算复杂度低于TDVSBM和BUVSBM,而在视频质量上总是优于TDVSBM且与BUVSBM几乎相当。     

求取真实运动矢量的一种算法

针对物体基视频编码与分割应用,提出了求取特征区域真实运动矢量的一种算法。首先,利用运动检测器将图像序列的每一帧分割成静止区域与运动区域。然后只对运动区域进行运动估计。为了克服传统块匹配法的固有缺点,本文算法采用变尺寸块匹配法,大的均匀区用大方块,运动物体的边缘区用小方块;在进行块匹配搜索期间,在易误匹配的区域,为了不遗漏真实的运动矢量,先按照ＭＡＥ准则预选出多个候选运动矢量,尽可能保证真实的运动矢量包含其中,接着再根据相邻块运动矢量的相关性,从多个候选运动矢量中选出一个比较可靠的运动矢量。实验结果表明,该算法产生的运动矢量真实可靠,而且一致性好,可用于识别可靠的运动区域。     

Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks.Extensive simulation results for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality and less complexity as compared to other ME algorithms. Implementation of proposed algorithm in JM12.2 of H.264/AVC shows reduction in computational complexity measured in terms of encoding time while maintaining almost same bit rate and PSNR as compared to Full Search algorithm.     

基于线性预测的半像素运动估计

在视频编码系统中,半像素精度的运动估计虽可以明显地改善编码效果,但也因此增加了不少运算量。为了降低运算量和提高半像素运动估计的速度,提出了一种全新的半像素搜索算法(half-pixel motion estimation based on linear prediction,简称BLPHME),其关键思想是通过分析整像素搜索和半像素搜索结果之间的相关性来建立一个线性模型,通过动态调整判决门限,以便预测并跳过那些不能从半像素块匹配搜索中得到好处的块。实验结果表明,该算法不仅可以明显地降低运动估计的运算量,同时还能得到与传统算法非常接近的图像质量和码率。此外,该算法还可以和基于整像素和半像素的快速运动估计算法一起使用,以进一步降低运算量。     

基于全局运动估计的视频对象分割算法

视频全局运动(摄像机运动)所表现的视频序列之间的时间相关性,较其它视频特征更能表达视频序列的高层语义信息.为了能够有效快速的得到视频的全局运动,通过对视频运动估计方法的研究,提出了一种新的基于奇异值分解(SVD)的视频全局运动估计算法.该方法首先通过块匹配法得到局部运动场,利用矩阵的奇异值分解估计全局运动参数,然后运用形态学运动滤波得到前景运动目标的粗略掩摸图像,最后综合利用此掩摸图像和边缘信息分割出运动目标.试验表明,提出的算法能够分割出具有全局运动特征的视频序列中的运动目标.     

一种基于块匹配的鲁棒运动估计算法

详细论述了基于块匹配的鲁棒运动估计算法。跟已有的基于块匹配的运动估计算法比较，首先，我们引入颜色信息来提高运动估计的准确性；其次，在更广泛的意义上运用自适应策略来减少计算量并同时保证算法的鲁棒性；最后，提出的基于预测修正的复合查找方法充分利用了物体运动的全局信息，克服了三步查找算法以及全查找算法的缺点并充分发挥它们二者的优点从而提高查找的效率和匹配精度。实验结果表明基于块匹配的鲁棒运动估计算法具有抗干扰能力强、运动估计准确、计算效率高等优点。     

Quad-Tree Motion Estimation in the Frequency Domain Using Gradient Correlation

We propose a new motion estimation scheme particularly suitable for broadcast-quality digital video applications due to its performance-complexity characteristics. The proposed scheme is based on the principle of gradient correlation and its computational efficiency is due to the fact that it operates in the frequency domain. The scheme involves the quad-tree decomposition of a frame thus providing a better level of adaptation to scene contents compared to fixed block size approaches. Quad-tree decompositions are obtained by using the motion compensated prediction error to control the partition of a parent block to four children quadrants. The partition criterion is applied iteratively until a target number of motion vectors or a target level of motion compensated prediction error is achieved or, ultimately, until no more than a single motion component can be identified. The partition criterion also guarantees a monotonic decrease of the motion compensated prediction error with an increasing number of iterations making our scheme suitable for progressive transmission and embedded coding applications. Our results show that our scheme outperforms fixed block size phase correlation as well as quad-tree motion estimation based on phase correlation in terms of rate-distortion characteristics yielding smoother motion vector fields as well as more compressible motion-compensated prediction residuals.     

基于HEVC的车辆异常事件检测

当前传统交通事故检测和查阅主要通过人工监测的方法,这种方法效率低且实时性差,本文提出一种基于最新压缩域视频编码标准HEVC（High-efficiency video coding）的车辆异常事件检测方法。首先对HEVC码流中提取出的运动矢量信息进行运动矢量累积迭代和中值滤波的预处理,之后根据提取出的块划分信息和运动矢量信息计算运动对象的运动强度,然后根据运动强度值和八连通区域法提取出运动对象,最后根据空间距离法和运动强度判别法检测出视频序列中发生的车辆异常事件。实验证明,该方法可以准确地检测出视频序列中发生的车辆异常事件;对于有着快速移动的运动目标以及多个运动目标的视频效果更好。     

一种基于矩不变的深度获取计算方法

针对传统矩不变深度信息获取方法在实际应用中普适性不足,且边缘检测效果不理想的问题,提出一种改进的矩不变深度信息获取方法。首先通过改变像距和光圈参数来获取不同位置的两幅图像,然后用基于图像梯度和灰度标准差相结合的方法检测出每幅图像的边缘,最后根据梯度图像占整幅图像的比值来计算深度信息。实验结果表明该算法提升了边缘检测的效果,减少了计算量,提高了距离估测的准确性和实时性。     

一种针对H.264/AVC的帧间模式快速选择算法

为了进一步减小宏块的帧间预测误差,新的视频编码标准H.264/AVC采用了可变块划分运动估计,极大地增加了编码器的复杂度.针对此问题,提出了一种快速的帧间模式选择算法,该算法利用已编码相邻宏块的时间和空间相关性信息预先检测图像,对图像各区域可能采用的编码模式进行判定,从而大大简化了模式选择的过程.实验结果表明,算法可以在图像质量基本保持不变的基础上,有效地降低编码的复杂度.     

Block-matching algorithm based on an adaptive reduction of the search area for motion estimation

The motion estimation and compensation technique is widely used for video coding applications but the real-time motion estimation is not easy due to its enormous computations. In this paper, a new adaptive reduction of search area for the block-matching algorithm is presented to reduce the computational complexity of the full search block-matching algorithm for low bit-rate video coding. The proposed method exploits the correlation of successive video frames and adjusts the size of search area depending on the displaced block difference and the block classification information in the previous frames of the block. Simulation results show that the proposed algorithm has similar mean square error performance to the full search block-matching algorithm but only requires less a half computational complexity than the full search algorithm.     

An Efficient Mode Decision Algorithm for H.264/AVC Encoding Optimization

The H.264 video coding standard significantly outperforms previous standards in terms of coding efficiency. However, this comes as a cost of extremely high computational complexity due to mode decision where variable block size motion estimation (ME) is employed. In this paper, we propose an efficient algorithm to jointly optimize mode decision and ME. A theoretical analysis is performed to study the sufficient condition to detect all-zero blocks in H.264, and thus adaptive thresholds are derived to early terminate mode decision and ME. Besides the aforementioned early termination technique, the proposed algorithm also introduces temporal-spatial checking, thresholds based prediction and monotonic error surface based prediction methods to skip checking unnecessary modes. Experimental results demonstrate that the proposed algorithm can significantly reduce the computational complexity of H.264 encoding while maintaining almost the same rate distortion (RD) performance as the original encoder     

Multisource surveillance video data coding with hierarchical knowledge library

The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulties to cope with complicated visual changes of objects resulting from various factors. In this paper, a novel hierarchical knowledge extraction method is proposed. Common knowledge on three coarse-to-fine levels, namely category level, object level and video level, are extracted from history data to model the initial appearance, stable changes and temporal changes respectively for better object representation and redundancy removal. In addition, we apply the extracted hierarchical knowledge to surveillance video coding tasks and establish a hybrid prediction based coding framework. On the one hand, hierarchical knowledge is projected to the image plane to generate reference for I frames to achieve better prediction performance. On the other hand, we develop a transform based prediction for P/B frames to reduce the computational complexity while improve the coding efficiency. Experimental results demonstrate the effectiveness of our proposed method.

     

动态模式的快速运动估计算法

针对H.264视频编码标准中运动估计的高计算复杂度,提出了一种动态模式的快速运动估计算法。该算法通过判断宏块的运动大小及运动方向选择相应的搜索模式;同时对标准中的中值预测进行了改进并提出了一种动态的参考块提前跳过策略。实验结果表明,该算法在保持良好的率失真性能的基础上,减少了运动估计时间,相对于快速全搜索算法FFS以及UMHexagonS算法,该算法分别减少了85.28%和35.29%的运动估计时间。     

一种内容完整的视频稳定算法

设计了一种基于可靠特征集合匹配的内容完整的视频稳定算法。为了避免运动前景上的特征点参与运动估计,由经典的KLT(Kanade-Lucas-Tomasi)算法提取特征点,而后基于特征有效性判定规则对特征点集合进行有效性验证以提高特征点的可靠性。利用通过验证的特征点对全局运动进行估计,得到精确的运动参数并据此对视频图像进行运动补偿。对于运动补偿造成的无定义区,首先计算当前帧的定义区与相邻帧的光流,以此为向导腐蚀无定义区;利用拼接的方法,填充仍为无定义区的像素。实验结果表明该算法对于前景物体运动具有较好的鲁棒性并能够生成内容完整的稳定视频序列。