基于三维滤波的视频去噪算法研究

视频去噪的目的是将原始视频从观测到的含噪视频中还原出来。对基于三维滤波的视频去噪算法进行了研究。首先利用贝叶斯阈值对视频序列的各帧在小波域中滤波,之后对帧间连续三帧图像进行帧间滤波。仿真结果表明该算法的有效性。     

基于Surfacelet变换视频自适应去噪

为了有效地保留视频去噪后的纹理细节,利用Surfacelet变换特性和自适应阈值的方法,提出了一种新的视频去噪算法。Surfacelet变换是超小波分析领域的最新发展,具有良好的多维信号处理能力,真正实现了对多维信号的多方向多尺度分解。本文介绍了Surfacelet变换的结构,根据Surfacelet变换的视频信号的系数特性和噪声分布特征,自适应估计每个方向子带的系数阈值。实验表明,本文算法能更好保留视频细节信息,对于视频的噪声也具有更强的抑制能力。     

Binocular perception based reduced-reference stereo video quality assessment method

A new reduced-reference (RR) stereo video quality assessment method is proposed in this paper by considering temporal characteristics of video and binocular perception in human visual system (HVS). Firstly, motion intensity is utilized to extract RR frames for the purpose of temporal characteristics in stereo video. Secondly, according to internal generative mechanism of HVS, fusion and rivalry in the process of binocular perception is modeled, and the RR frames are divided into binocular fusion portion and binocular rivalry portion. Then, RR frame quality indicators are computed for these two portions. Finally, the RR frame quality indicators of the original and distorted frames are compared. A temporal pooling strategy is utilized on these quality indicators to obtain final stereo video quality score, where the motion intensity is used for toning the pooling parameters. Experimental results show that the proposed method has better performances when compared to other state-of-the-art quality assessment methods.     

基于视频去噪算法移动机器人视觉定位研究

提出基于视频去噪算法的移动机器人的视觉定位方法,应用运动补偿时域视频去噪算法对视频图像去噪,提取去噪视频图像的图像像素,用摄像机标定方法确定目标空间坐标,扩展卡尔曼滤波Matlab仿真算法对目标进行定位,有效地解决了视频图像的拖尾、重影等现象,实现目标动态定位。实验结果表明视频去噪效果良好,摄像机标定准确,目标定位精确度高,达到了预期效果。     

基于感知的多视点视频编码宏块模式选择快速算法

多视点视频编码(MVC)采取可变块模式选择技术和 多参考帧技术显著提高了编码的压缩效率,但同时带来了巨大的 编码计算复杂度。为了降低MVC的计算复杂度,提出基于感知的快速MVC宏块模式选择 算法。基于人眼视觉感知的特点,利用视觉恰可察觉失真(JND)的概念建立MVC宏块的最优模 式和JND的联系,并利用该联系确定早期结束最优宏块模式选择过程的阈值,根据当前编码 宏块的JND与阈值的 关系自适应地减少每个编码宏块的模式搜索次数,进而减少MVC的方向搜索和参考帧搜索的 次数,以降 低编码的复杂度,提高MVC速度。实验结果显示,对于不同运动特性、内容、纹理信息、相 机间距和图像 尺寸的测试序列,提出的快速算法在率失真性能几乎不变的情况下平均节约76.00% 编码时间。     

A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL

A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed.The encoder combines the best features of Fine Granularity Scalabla (FGS) coding,frame-dropping coding,video redundancy coding,and human visual model.According to packet loss and available bandwidth of the network,the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model,rate shaping,and periodically inserting key frame.The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm.It is shown that RAVE is a very efficient robust videl encoder that provides improved visual quality for the receiver and consumes equal or less network resource.Results are confirmed by subjective tests and simulation tests.     

一种基于人眼视觉注意力的三维视频重定向方法

从人眼视觉注意力的角 度出发,提出了一种三维视频重定位方法。首先,引入场景的深度信息,并结合梯度、显著 性和运动对象检 测构造模型,得到视觉注意力能量项;然后,分别构造帧内能量项和帧间能量项,并结合视 觉注意力能量项, 构造总能量函数进行细缝裁剪,实现视频的重定向。实验结果表明,本文方法能够较好地保 护视频场景中 的重要对象,并避免较大的失真和抖动。     

Video fusion performance evaluation based on structural similarity and human visual perception

In order to evaluate different video fusion algorithms in temporal stability and consistency as well as in spatial information transfer, a novel objective video fusion quality metric is proposed with the structural similarity (SSIM) index and the perception characteristics of human visual system (HVS) in this paper. Firstly, for each frame, two sub-indices, i.e., the spatial fusion quality index and the temporal fusion quality index, are defined by the weighted local SSIM indices. Secondly, for the current frame, an individual-frame fusion quality measure is obtained by integrating the above two sub-indices. Lastly, the proposed global video fusion metric is constructed as the weighted average of all the individual-frame fusion quality measures. In addition, according to the perception characteristics of HVS, some local and global spatial-temporal information, such as local variance, pixel movement, global contrast, background motion and so on, is employed to define the weights in the proposed metric. Several sets of experimental results demonstrate that the proposed metric can evaluate different video fusion algorithms accurately, and the evaluation results coincide with the subjective results well.     

基于视觉感知特性的红外图像质量评价方法

根据红外成像特点,设计了一种基于视觉感知特性的红外图像质量评价算法。该算法结合人眼视觉和红外图像的结构信息对图像的失真程度进行描述,通过提取图像的边缘特征、对比度特征,然后利用视觉显著模型对特征进行差异融合,从而实现对红外失真图像的质量评测。实现结果表明,本文方法可对失真红外图像进行有效评价,与传统方法相较,此评价指标与人眼主观感知更一致。     

基于噪声估计的自适应块匹配和三维滤波降噪算法

针对经典的块匹配和三维滤波(BM3D)降噪算法中最 为核心的噪声水平(方差)参数在使用中需要 人工手动设置极大影响了降噪效果并限制了它的应用,提出了一种新的基于自然场景 统计(NSS)的噪声水平特征矢量和支持向量回归(SVR)技术的快速噪声水平估计算法并应用于 经典BM3D算法 中,使之转变为自适应降噪算法(Adaptive BM3D)。本文算法首先利用小波变换对图像进行 不 同尺度和不同方向的分解,提取各子带滤波系数并用通用高斯分布模型(GGD)建模,以模型 参数构成反映噪 声图像噪声水平的特征矢量;然后用SVR方法在大量噪声图像样本上进行训练获得图像噪声 水平预测模型。 实验表明:改进后的ABM3D算法实际图像降噪效果比BM3D算法获得进一步提升,并且仍然 保持了非常高的执行效率,相对于当前各主流算法具有明显的竞争力。     

基于红外智能视频检测的自适应加速检测算法

随着人工智能技术的快速发展,智能车辆辅助驾驶系统已经逐渐走进人们的生活。基于可见光成像的智能车辆辅助驾驶系统虽然日趋成熟,但是其受限于可见光成像条件的制约,在黑暗、雨雾等条件下,使用效果可能大打折扣甚至无法使用。红外智能车辆辅助驾驶系统因成像依靠红外成像设备,使其成为解决这一问题的重要手段而受到广泛关注。但是红外智能车辅系统在一些特殊情况下,如车辆突然提速、应用场景发生较大变化等,其检测及成像实时性可能会受到影响进而导致显示画面卡顿。上述情况的出现会严重影响驾驶员判断甚至行车安全。针对上述情况本文提出了一种自适应隔帧检测算法可以在红外智能车辅系统应用过程中针对不同情况出现的检测速度波动,自主调整检测间隔帧数,以保证检测和成像的实时性。实验证明,该算法对于视频检测速度提升有效,具有一定的实用性。     

基于视觉特性的灰度级自适应盲水印算法

提出了一种基于小波变换和视觉感知特性的灰度级自适应盲水印算法。该算法利用视觉系统的背景亮度、纹理和边界掩盖特性把载体图像的小波系数分为18类；在对256级灰度水印图像压缩后。依据载体图像小波系数的分类，以不同强度嵌入到载体中。该算法在抽取水印时不需要原始载体图像和原始水印。实验结果表明此算法可有效地隐藏灰度水印图像，并有较好的顽健性，能抵抗裁剪、JPEG压缩、加噪、图像增强、滤波等多种处理。     

Reduction of blocking effect in DCT-coded images based on a visual perception criterion

Blocking effect constitutes one of the main drawbacks of the actual DCT-based compression methods. We propose in this paper a new block reduction technique; it is based on a space-variant non-linear filtering operation of the blocking artifacts present in the image to be reconstructed. To account for the perceptual importance of the distortion, the amount of smoothing is adapted to the visibility of the blocking effect. A visibility parameter is computed for each artifact using the psychovisual properties of the human visual system (HVS). The postprocessing algorithm is in conformity with actual existing compression standards; it provides a way to greatly reduce the artifacts without degrading high-frequency information of the original image. First the proposed method is described and then experimental results are presented, showing the effectiveness of the correction.     

基于视觉感知的图像增强质量客观评价算法

提出了一种新的图像增强质量客观评价算法.算法综合考虑图像局部区域的背景平均亮度和空间复杂度对视觉分辨力的影响,导出了用于判断局部灰度级跃变是否被有效感知的重要参数--临界可见偏差JND(Just Noticeable Difference),并利用此参数分别计算出图像增强前后有效感知像素数的变化情况,从而定量地给出图像增强质量的评价结果.测试结果表明,本文算法的性能优于传统的平均局部方差法,且评价结果与主观评价结果吻合.     

Depth video spatial and temporal correlation enhancement algorithm based on just noticeable rendering distortion model

Spatial and temporal inconsistency of depth video deteriorates encoding efficiency in three dimensional video systems. A depth video processing algorithm based on human perception is presented. Firstly, a just noticeable rendering distortion (JNRD) model is formulated by combining the analyses of the influence of depth distortion on virtual view rendering with human visual perception characteristics. Then, depth video is processed based on the JNRD model from two aspects, spatial and temporal correlation enhancement. During the process of spatial correlation enhancement, depth video is segmented into edge, foreground, and background regions, and smoothened by Gaussian and mean filters. The operations of the temporal correlation enhancement include temporal–spatial transpose (TST), temporal smoothing filter and inverse TST. Finally, encoding and virtual view rendering experiments are conducted to evaluate the proposed algorithm. Experimental results show that the proposed algorithm can greatly reduce the bit rate while it maintains the quality of virtual view.     

A neutrosophic filter for high-density Salt and Pepper noise based on pixel-wise adaptive smoothing parameter

Image indeterminacy has been neglected in most traditional filtering algorithms. This paper proposes a pixel-wise adaptive neutrosophic filter based on neutrosophic indeterminacy feature to remove high-level Salt-and-Pepper noise. In the proposed algorithm, the indeterminacy of a pixel is quantified by a Neutrosophic Set and innovatively exploited as an efficient characteristic of measuring the similarity of pixels. In order to adjust the smoothing parameter of the weight function pixel-wise adaptively, the uncertainty of a pixel is utilized as an indicator of image contents. Extensive experiments on numerous images demonstrate that with a 3 × 3 window, our method outperforms many existing denoising methods in terms of noise suppression and detail preservation.