No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

一种改进的新三步搜索算法

在对三步法工作原理进行研究的基础上提出了新三步搜索算法（NITSS）。该方法充分利用视频序列运动矢量概率分布上的中心偏置特性．在三步搜索算法的基础上引入了十字型分布的4个点构成搜索点群。实验结果表明。新三步搜索算法解决了三步法的小运动估计效果较差问题．提高了搜索精度，保持了三步法的高效率。     

小型机载数字视频记录系统的设计与实现

采用嵌入式控制模块SOM2386和专用视频处理芯片SZ1510,设计和实现了一种基于电子硬盘存储的嵌入式小型机载数字视频记录系统。给出了系统总体方案,阐述了视音频解码编码、接口、控制及存储单元的硬件设计,介绍了系统的软件设计,并给出了系统软件的整体工作流程。该系统体积小、重量轻、抗震性好、使用维护方便,已在某型飞机上得到应用。     

Wireless video streaming with TCP and simultaneous MAC packet transmission (SMPT)

Video streaming is expected to account for a large portion of the traffic in future networks, including wireless networks. It is widely accepted that the user datagram protocol (UDP) is the preferred transport protocol for video streaming and that the transmission control protocol (TCP) is unsuitable for streaming. The widespread use of UDP, however, has a number of drawbacks, such as unfairness and possible congestion collapse, which are avoided by TCP. In this paper we investigate the use of TCP as the transport layer protocol for streaming video in a multi‐code CDMA cellular wireless system. Our approach is to stabilize the TCP throughput over the wireless links by employing a recently developed simultaneous MAC packet transmission (SMPT) approach at the link layer. We study the capacity, i.e. the number of customers per cell, and the quality of service for streaming video in the uplink direction. Our extensive simulations indicate that streaming over TCP in conjunction with SMPT gives good performance for video encoded in a closed loop, i.e. with rate control. We have also found that TCP is unsuitable (even in conjunction with SMPT) for streaming the more variable open‐loop encoded video. Copyright © 2004 John Wiley & Sons, Ltd.     

一种高分辨率视频转换系统的设计

介绍了一种视频信号转换系统,本系统能将医学专用显示设备上播放的非标准单色模拟视频信号转换为标准电视视频信号、标准SVGA视频信号,也可直接输出标准数字视频信号。使非标准单色模拟视频信号能够在通用隔行或逐行显示设备上显示,解决了医学系统产生的视频信号在教学、演示或传输方面的需要。本系统在多家医院试用,达到设计功能。