多标准视频编码器下的运动估计评估

运动估计是视频编码过程中最为复杂和耗时的阶段。为分析和优化其性能,从多个流行的开源视频编码器中提取出单独的运动估计算法模块,根据视频分辨率和视频内容的不同建立程序输入集,从而构成一套完整的测试程序集合。利用性能分析工具对算法性能和微体系结构性能进行量化分析,给出这些算法在当今主流处理器体系结构上的性能差异。实验结果表明,复杂视频和高分辨率视频下的运动估计算法耗时最长,且大部分算法的指令级并行性没有太大差异。算法最后一级高速缓存的缺失率和分支误预测率都较低,分别在0.01%和7%以下。     

面向实时视频编码应用的控制系统

从提高外设与内核并行性的角度引入流水线的思想,实现基于查询和乒乓缓冲的控制架构,针对结构中存在的问题提出改进的双级控制架构,在该架构下实现基于H.264标准的实时视频编码。实际测试表明,该系统能在保证系统稳定性的同时,有效提高编码器的编码效率。     

H．264运动估计算法在Imagine流处理器上的加速实现

鉴于Imagine流处理器在媒体处理领域取得的优越性能，我们将H．264视频编码中耗时较大的运动搜索算法映射到Imagine上，利用流处理的思想充分开发其并行性来提高编码性能，对QCIF图像编码可达372fps，加速效果是非常明显的。由此证明，H．264是适于Imagine实现的。     

一种异步视频对象的速率控制算法

MPEG-4提出了视频对象的概念，产生了基于对象的速率控制问题，编码的视频对象可以是同步进行也可以是异步进行，后者在低比特率条件下更具有优越性，实验结果也证实了这点，针对异步视频对象编码，提出 一种速率控制算法，以达到恒定目标比特率，该算法首先根据在一个短的时间段内，一个视频对象的同一类型编码比特数近似相等的假设来估计某时刻总的编码比特数，然后采用全局分配原则，将目标比特数分配到编码的视频对象中，以获得视频对象近似恒定的失真比，另外，还提出了一种后处理技术，并用以克服异步对象的解码合成图象的失真，实验结果表明，该算法能够对异步视频对象进行有效的比特控制，并具有较高的主客观质量。     

MPEG-4视频泵中Transcoding机制与实现

视频泵技术是视频服务器设计中的关键技术之一。MPEG-4视频泵的Transcoding(转换编码)机制是指在视频服务过程中，将用户请求的其它格式的视频数据实时地转换为MPEG-4视频格式。该文在MPEG-4标准的技术框架下，对MPEG-4视频泵功能进行了扩充设计，使其具有Transcoding功能，并只在首次点播服务时实现转换编码和存储。具有Transcoding功能的MPEG-4视频泵在视频服务器中的应用，是实现MPEG-4视频服务的技术核心之一。     

H.26L视频编码器并行性分析与实现

文中对H.26L的并行性进行了分析，提出了几种不同的并行调度策略，以实现视频编码的实时处理。首先对H．26L编码器新特点进行了简单的介绍，然后从不同的层次提出了几种不同的并行调度策略并作了相应的分析；最后给出了其中一种并行调度的实现及其在COW上的试验结果。     

H.264视频编码软件的并行化处理

由于视频编码的复杂性导致对计算能力的更高需求,越来越多的应用投入到视频编码的并行算法研究中。该文从H.264视频编码标准的特点出发,介绍了H.264视频编码端并行化处理:给出了并行编码器设计的主导思想,并行任务单元的选择,讨论了GOP级和Slice级的并行化处理。     

播放我最懂 ffdshow大学堂

ffdshow是现在使用广泛的多功能解码器,可以解码Divx、Xvid、WMV等十几种不同的视频编码格式。是不是所有人都能使用好ffdshow,这就是个疑问了,其实要使用好ffdshow解码器,就需要了解ffdshow中禁用和调用的故事,下面笔者就一一给大家讲述ffdshow中禁用于调用的二三事吧!     

Porable Media Center个人随身影院指南——What can you do 视频播放器能播放什么视频？

我们当然希望播放器所播放的视频品质越高越好，甚至可以播放高清晰度格式的（HDTV）视频，但显然这个要求有些过高了，因为播放高清视频不仅对存储容量要求很高，而且对解码器的品质也有很高要求，以至于目前我们还没有见到过能够满足这种要求的产品，当然你也不必因此耿耿于怀，事实上常见的MPEG-4（Divx, Xvid）也是一种相当不错的编码方式，     

利用VC~( )实现局域网实时视频传输

文中针对不同的局域网,提出一种通用的实时视频传输的解决方案。在使用Divx编解码的基础上,提出了从压缩、组帧、发送到接收、解压整个流程的思想,具体实施方案和VC 实现核心源代码以及传输控制策略,有效地保证了高质量的实时视频传输。     

基于EM8623L的高清媒体信息发布终端设计

以Sigma Designs公司高清解码芯片EM8623L为核心,设计了一种嵌入式高清媒体信息发布终端.它支持MPEG4、WMV9、Divx、H.264、VC-1等多种视频文件播放,能够实现视频、音频、图片和滚动字幕等多种媒体信息的组合播放,播放质量可达到卓越的高清电视品质.本文对该系统的软硬件设计进行了详细介绍,实践证明该方案具有成本低、功能强、清晰度高等特点.     

TheXYZAlgorithm For Real-Time Compression of Full-Motion Video

In this paper, we describe a new full-motion video compression algorithm, based on three-dimensional Discrete Cosine Transform. The algorithm, calledXYZ-video compression, takes advantage of statistical behaviour of video data both in spatial and temporal domains. We demonstrate the algorithm by implementing it in software and comparing it with the MPEG video compression standard. The results show a significant superiority of the proposedXYZ-video compression algorithm compared to the MEPG standard. This superiority is shown in several key categories: (i)XYZ- video compression gives much higher compression ratios than the MPEG standard for the same quality of the decompressed video, and (ii) the compression time forXYZalgorithm is much shorter than for the MPEG standard. This suggests that theXYZ-video compression scheme can be efficiently applied for real-time video compression.     

HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs.     

Scalable video coding using motion-compensated temporal filtering and intra-band wavelet based compression

Scalable video compression is a crucial task that allows for high flexibility of video streams to different networks in various applications. Current video coding techniques exploit temporal correlation using motion-compensated predictive or filtering approaches. Particularly, motion-compensated temporal filtering (MCTF) is a useful framework for scalable video compression schemes. In this paper, we propose a new scalable video coding method that combines open-loop motion-compensated prediction with an embedded intra-band wavelet based compression. Our major objective is to provide a wavelet based video coding system that circumvents the drawbacks of conventional closed-loop prediction systems, without sacrificing compression performance. To improve the coding efficiency, we adaptively weight the target bitrate according to the temporal frame position in the temporal pyramid. Comparisons with state-of-the-art scalable video coding solutions confirm an overall coding efficiency gain of the proposed method specially at high bitrates.     

Learning-based high-efficiency compression framework for light field videos

The massive amount of data usage for light field (LF) information poses grand challenges for efficient compression designs. There have been several LF video compression methods focusing on exploring efficient prediction structures reported in the literature. However, the number of possible prediction structures is infinite, and these methods fail to fully exploit the intrinsic geometry between views of an LF video. In this paper, we propose a deep learning-based high-efficiency LF video compression framework by exploiting the inherent geometrical structure of LF videos. The proposed framework is composed of several crucial components, namely sparse coding based on a universal view sampling method (UVSM) and a CNN-based LF view synthesis algorithm (LF-CNN), a high-efficiency adaptive prediction structure (APS), and a synthesized candidate reference (SCR)-based inter-frame prediction strategy. Specifically, instead of encoding all the views in an LF video, only parts of views are compressed while the remaining views are reconstructed from the encoded views with LF-CNN. The prediction structure of the selected views is able to adapt itself to the similarity between views. Inspired by the effectiveness of view synthesis algorithms, synthesized results are served as additional candidate references to further reduce inter-frame redundancies. Experimental results show that the proposed LF video compression framework can achieve an average of over 34% bitrate savings against state-of-the-art LF video compression methods over multiple LF video datasets.

     

Combining Active and Semi-Supervised Learning for Video Compression

Video compression algorithms manipulate video signals to dramatically reduce the storage and bandwidth required while maximizing perceived video quality. Typical video compression methods include discrete cosine transform, vector quantization, fractal compression, and discrete wavelet transform. Recently, a machine learning based approach has been proposed which converts the color images (frames) to gray scale images (frames) and the color information for only a few representative pixels is kept. A learning model is then trained to predict the color values for the gray scale pixels across frames. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose to combine active and semi-supervised learning for video compression. The basic idea is to minimize the size of the covariance matrix of the regularized least squares estimates, in which the regression model assumes that each pixel can be reconstructed by the other pixels with similar spatial location and intensity value. The experimental results demonstrate the effectiveness of the proposed approach for video compression.     

A new DCT audio watermarking scheme based on preliminary MP3 study

In this paper, a new audio watermarking scheme operating in the frequency domain and based on neural network architecture is described. The watermark is hidden into the middle frequency band after performing a Discrete Cosine transform (DCT). Embedding and extraction of the watermark are based on the use of a back-propagation neural network (BPNN) architecture. In addition, the selection of frequencies and the block hiding the watermark are based on a preliminary study of the effect of MP3 compression at several rates on the signal. Experimental results show that the proposed technique presents good robustness and perceptual quality results. We also investigate the application of the proposed technique in video watermarking. Traditional techniques have used audio channel as supplementary embedding space and adopt state-of-the art techniques that resist to MP3 compression attack. In these techniques, the MPEG compression attack is only evaluated on the video part and the audio part is kept unaffected. In this paper, we adapt the preliminary MP3 study to video watermarking technique but with a preliminary study of the MPEG compression applied to the audio channel. Here again, we notice that the application of the preliminary MPEG study to the audio channel improves the robustness of the video watermarking scheme though keeping high-quality watermarked video sequences.     

Effective and Efficient Video Compression by the Deep Learning Techniques

Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video.     

结合一阶自回归滑动平均和压缩感知的视频模型

摘要：目的： 针对视频处理中面临的采样数据量大及采样时间长的问题,本文把视频状态空间一阶自回归滑动平均模型和压缩感知模型相结合,提出了一种基于一阶自回归滑动平均的视频压缩感知模型。方法： 其主要思想是：在压缩感知理论框架下,充分利用视频帧内稀疏性和帧间相关性,把视频分割成动态部分和静态部分同时采样但分别处理,利用凸优化等方法得到视频状态空间一阶自回归滑动平均模型的关键参数。结果：多组真实场景下的实验结果表明,该模型较大程度上降低了帧间冗余度和数据采集量,视频采集压缩比在100至200时,仍然能取得较好的重建效果。结论：本文结合压缩感知和线性预测技术,提出了一种新的视频获取模型,对视频的静态部分和动态部分分别处理,并给出了该模型使用的条件。实验表明,该模型对帧间变化不大的视频,具有良好的压缩效果。     

基于H.263的WEB-CAMERA嵌入式系统

由于多媒体技术的飞速发展 ,越来越多的领域需要一种高效、实时数字图像压缩技术。现有的数字图像压缩技术都比较复杂 ,而嵌入式系统的是有限的。这就要求根据嵌入式系统的特点和图像的内容对数字图像压缩算法进行改进。该文介绍了如何根据嵌入式系统的特点和图像的内容对数字图像压缩算法进行改进。主要根据视频序列的统计特性和高级数字图像压缩比特—误差分析法 ,来降低数字图像压缩算法的复杂度。所以在嵌入式系统实现WEB -CAMERA ,该系统利用CMOSSENSOR采集图像 ,通过AD公司Blackfin2 15 3 5DSP实现H .2 63编码器且将uClinux移植到ARM7TDMI核的CPU芯片内 ,最终实现WEB -CAMERA。