对于视频市场，Google早有自己的打算，当今，视频应用早已成为普罗大众每天在互联网上的最常见的应用之一，并且视频应用也是很多Google产品中重要的组成部分. 收购ON2的原因在于, YouTube带宽成本太高, ON2的视频压缩技术有助于减少任何视频所使用的带宽, 并且On2可以使Google将视频压缩软件整合到其面向手机的Android系统, 以及即将在2010年发布的面向上网本的Chrome OS系统。

不得不说, Google的野心实在是大, 曾经我们或许会对Google不断推出免费的新产品和服务的多元化经营感到疑惑, 直到Google宣布Chrome浏览器的发布, 一切疑问豁然开朗. 由最开始的Google Search, 到一鸣惊人的Gmail, Gtalk, Google Docs, Picasa…, 最后到完成战略布局的Google Chrome浏览器, 其逐步朝着能”一统江湖”的网络帝国迈进.

如今我们的网络生活可谓难以离开Google的产品与服务了, 至少对于我, 已经被Google深度套牢了, 以下的活动主导了我的网络生活: 打开Chrome浏览器, 利用Google Search进行一般搜索, 用Google Scholar进行学术搜索, 登录Gmail检查邮件了, Gtalk在线聊天, 输入法用Google拼音, 日历用Google Calendar, 在线文档编辑可以用Google Docs, 图片浏览与存储用Picasa和Picasa Web Albums, 新闻阅读用Google Reader, 网络视频用YouTube, 网络地图用Google Maps, 打VoIP电话已经可以用Google Voice了.

由此可见,基本上可以抛弃臃肿的Windows系统了,完全转向使用Mac OS X系统了.

• Practical experience 1: MPEG-2, a integrate part of video subgroup when under WG 11 auspices;
• Practical experience 2: H.264/AVC (JVT), a distinct subgroup of WG11.

However, the joint meeting between MPEG and VCEG did not reach a conclusion on which one to use. The naming of output specification and collaborative team were also discussed, but no conclusion was drawn at this stage.

The objective experimental results show that 20% average bit reduction is achieved compared with H.264/AVC High Profile for all classes of test video sequences (Class A: 19%, Class B:25%, Class C:22%, Class D: 15% bit reductions, respectively). Subjective evalution is also conducted during this meeting. The purpose of subjective evaluation is not ranking of response proposals, but identifying examples that give best evidence and assessing whether the evidence is large enough. The subjective evalution results also show that there is positive evidence to support the availability of suitable technology to justify the start for a new standardization activity.

Therefore, MPEG plans to move forward toward quickly issuing a formal Call for Proposals (CfP) on HVC (as soon as October 2009). The timeline of CfP on HVC:

• July 20, 2009: Availability of test materials defined in Draft CfP
• August 15, 2009: Availability of preliminary anchors
• October 27, 2009: Deadline for Pre-registration (mandatory)
• October 31, 2009: Final Call for Proposals (public)
• November 15, 2009: Formal registration
• December 15, 2009: Coded test material available at the test site
• January 1, 2010: Subjective assessment starts
• January 11, 2010: Registration of documents describing the proposals with MPEG video chair
• January 12, 2010: Submission of document to MPEG Video chair
• January 16, 2010: Report of the subjective test results

AvsX decoder部分的源代码加入了该网址 http://sourceforge.net/projects/avsx/,下一步的计划,继续将AvsX encoder部分的源代码逐步加入.

由于一个人的力量有限,对AvsX的编解码器的优化难以完善,希望有更多对AVS开源编解码器有兴趣的朋友能够加入该项目. 如果想参与到这个项目或者修改源代码的，你可以把你的代码email给我,或者把sourceforge的id发给我,我给你分配sourceforge的项目权限. 谢谢！

代码的svn为: svn co https://avsx.svn.sourceforge.net/svnroot/avsx avsx

The source code of this decoding library is available here. Further information can be found here about installation, features and additional tools related to the SVC decoder library. The SVC decoder is conformant to the following sequences, see also the IETR conformance entry in this tabular. This decoder has been also ported over several platforms such as PDAs and DSP from TI. It can serve as a basis for future development in MPEG RVC (Reconfigurable Video Coding).

下载地址：avsxdecore-r1-b20090617.zip

AvsXCore项目下一步开发计划为，采用NVIDIA CUDA架构，实现基于GPU加速的AVS视频解码器，以达到1080p的实时解码及降低CPU的占用率的目标。

• AhGs will meet on 27-28 June 2009.
• The 89^th MPEG Meeting will be held on 29 June – 3 July, 2009.
• The 31^st JVT Meeting will be held on 29 June – 3 July, 2009.
• The 38^th ITU-VCEG Meeting will be held on 1-2 July, 2009. (Note: this VCEG meeting only discusses the future collaboration of VCEG and MPEG. Technical issues will be discussed in Geneva meeting on July 6 – 8.)

I will go to London to attend those meetings, and will report the progress of meetings in good time. Keep an eye on this website!

1. 首先安装NVIDIA最新的显卡驱动,以及 CUDA Toolkit 和 CUDA SDK. 下载地址 here.
2. 安装CUDA之后，你可以测试一下你的机器是否CUDA配置正确。方法如下：进入CUDA安装目录，我将CUDA安装在/Developer目录下。在/Developer/CUDA下有一个Makefile，在该目录下运行make，编译projects目录下的所有demo程序，其中就包括一个deviceQuery程序。编译完成后，你可以在/Developer/CUDA/bin/darwin/release/目录下运行deviceQuery程序，其应该输出以下信息; 否则，你的机器并没有CUDA capable的GPU，或者GPU设备驱动并没有正确安装。
   
3. 下载NVCuda Plugin for Xcode，下载地址here.
4. 解压缩nvcuda_plugin.zip包，将里面的NVCuda.pbplugin文件拷贝到
   “/Library/Application\ Support/Developer/Shared/Xcode/Plug-ins/“ 目录下， 如果你没有这个目录，那么在这个路径上创建相应的目录结构。重新启动你的Xcode后，在你的工程Target的Build Tab下应该有一个叫NVIDA Cuda – Code Generation的Section了。
5. 打开Xcode，创建一个Command Line Utility下的C++ Tool的工程，将你的源代码拷贝到该工程的目录下并加入工程。
6. 在菜单Project -> Edit Active Target下的Build tab进行以下设置
   在Section: Linking中的Other Linker Flags添加-lcuda, -lcudart, 并选中Prebinding
   以及Section: Search Paths中:
   在Header Search Paths中添加CUDA的系统目录/usr/local/cuda/include/**,如果你用到了CUDA SDK里面的函数,则需要加上/Developer/CUDA/common/inc.
   在Library Search Paths中添加CUDA的系统目录/usr/local/cuda/lib,如果你用到了CUDA SDK里面的函数,则需要加上/Developer/CUDA/lib 和/Developer/CUDA/common/lib
   以及Section: NVIDA Cuda – Code Generation中,Host Compilation设置为c++.

   

7. 点击Build and Go 按钮，你的程序应该不会再有”no rule to process file test.cu … for architecture i386″的错误了。
8. 如果你用到了CUDA SDK的函数,有可能会出现Link时找不到相应函数,这时你需要将CUDA SDK的库加入你的工程中,如libcutil.a库文件.
9. Have Fun！

Previously, adaptive rounding was proposed to improve quantization, which captures the statistics of the incoming residual signal and adjusts the rounding offsets accordingly. However, the adaptive rounding quantization is still based on the criterion which minimizes the mean-squared quantization error between the original signal and the quantization reconstructed signal. From the sense of rate-distortion optimization, the cost from the rate should also be considered.

The basic idea underlying the rate-distortion optimized quantization is to minimize a cost function D+ λR such that both the rate R and the distortion D are considered in coding decisions. For quantization case, the RD optimal coding is to solve a minimization problem of

(7)

where S is the original signal, and T^-1 denotes the inverse transform operation. Consider that the DCT is a unitary transform, which maintains the Euclidean distance. We use the Euclidean distance for D(.) to avoid the inverse DCT computation. Thus, the above equation becomes

(8)

where W=T(S), and T is the forward transform operation.

A simplified rate-distortion optimized quantization scheme is proposed and implemented in KTA software. In the RDO-Q, assuming the transform coefficients before quantization are W_i, (i=0,…,M-1), then the quantized coefficients/levels Z_i (i=0,…,M-1) are calculated as follows:

1. For a given coefficient position k, k=M-1,…,0, assume that coefficient W_k is the last significant coefficient in the block:
   • For each coefficient W_i, i=k-1,…,0, calculate its Lagrangian cost when the quantized value Z_i is equal to 0, Z_floor and Z_ceil, as shown in Figure 7. The Lagrangian cost J_k,i(λ,Z_i) when coefficient W_i is quantized to Z_i is calculated as:
     (9)
     where err(W_i , Z_i) is the quantization error if the coefficient W_i is quantized to value Z_i, measured by  Euclidean distance; And bits(Z_i) is the number of bits needed to code Z_i. The value of Z_floor and Z_ceil are defined as:
     (10)
     (11)
2. Let the final quantized level Z_i,opt=argmin J_k,i(λ,Z_i) and update Lagrangian cost J_k(λ) using J_k,i(λ,Z_opt).
3. The final quantized vector of quantized coefficients Z_opt=argmin J_k(λ).

Figure 7. Possible quantized values in RDO-Q

To speed up the algorithm the following simplifications are made:

1. If coefficient W_k is closer (measured as absolute difference between the coefficient and its reconstructed value) to Z_floor than to Z_ceil only value Z_floor is considered.
2. If coefficient W_k is closer to Z_floor than to Z_ceil, and Z_floor is equal to zero, coefficient W_k can not be the last nonzero coefficient. Hence the calculation of Lagrangian cost J_k(λ) is skipped for this value of k.
3. The calculation of J_k(λ) is stopped when J_k(λ) starts to increase with decreasing k.

Here, we only discuss the entropy coding by using CABAC, and for the CAVLC a similar estimation method for the number of bits can be applied. To estimate the number of bits required to code a coefficients, we use the tabularized values of entropy of the probabilities corresponding to states in CABAC arithmetic coding engine. But, the CABAC algorithm uses the context modeling. That is, the coding of current coefficient in a block is related to the state of previous coded coefficients.

In general, CABAC consists of three steps, i.e.,

1. Binarization. The so-called UEG0 algorithm is used to convert non-zero transform coefficients into a binary representation so that the binary arithmetic coding engine can be used to code them.
2. Context modeling. CABAC defines a probability model for each binary bit.
3. Binary arithmetic coding. The binary representation is encoded bit by bit using corresponding models.

The RDO-Q is closely related to the context modeling for residual coding. Residual coding by CABAC includes two parts, i.e., coding a so-called significance map and coding non-zero coefficients. Given a zigzag ordered sequence of transform coefficients, its significance map is a binary sequence which indicates the occurrence and location of the non-zero coefficients. The context modeling for coding the significance map is associated with the zig-zag order, and is easy to be included in RDO-Q. The context modeling for coding non-zero coefficients, however, is complicated. For a given sequence, there are in total 10 contexts for coding non-zero coefficients, with 5 of them for coding the first bit of a binary representation and the other 5 dedicated to coding the second to 14th bits. Briefly, contexts are selected as follows,

1. Scan the sequence in the inverse order to initiate two parameters as NumLg1 and NumEq1. NumLg1 is the number of coefficients that are greater than 1 while NumEq1 accords to those equal to one.
2. The context for the first bit is determined by
   (12)
3. The context for the 2-14th bits is selected by
   (13)

There is also a bypass mode with a fixed distribution. Other bits in the binary representation use the bypass mode.

Figure 8. The graph structure for RDO quantization based on CABAC in H.264/AVC

In order to solve the minimization problem, a graph-based algorithm is used to address the computation of the rate function R(.) of CABAC. As shown in Figure 8, the graph is constructed based on coding features of CABAC. Basically, states are defined based on the context model selection. Thus, states are named by values of NumEq1 and NumLg1, as of NumEq1_NumLg1 , e.g., 2_0 accords to NumEq1 = 2 and NumLg1 = 0. When NumLg1 > 0, the context is irrelevant to NumEq1. Thus, there are three states as X_1, X_2, and X_3. The context is fixed for all NumLg1 ≥4. Accordingly, one state X_X is defined. For a 4×4 luma block, there are 16 columns, each of them corresponding to one coefficient. In each column there are up to 8 states. Transitions are established between states according to the increase of NumEq1 and NumLg1 , e.g., the state 1_0 is connected to 1_0, 2_0 and X_1 according to a quantization output of 0, 1, or greater than 1, respectively. In case that the quantization output is greater than 1, parallel transitions are established so that each accords to a unique value. In practice, because the distortion is a quadratic function with respect to the quantization output, it is sufficient to investigate only a few parallel transitions. Thus the complexity is greatly reduced without sacrificing the optimality. Finally, a graph structure as shown in Figure 8 is obtained.

The optimal RDO quantization design now becomes a problem to search for a path in the graph for the minimal RD cost. It is not hard to see that the above graph design allows an element-wise additive computation of the RD cost. The Viterbi algorithm is then used to do the search, which leads to the solution of the minimization problem.

References:

[1].    Gary J. Sullivan, “Adaptive quantization encoding technique using an equal expected-value rule”, Hong Kong JVT meeting contribution JVT-N011, January 2005.

[2].    E.-h. Yang and X. Yang, “Rate distortion optimization of H.264 with main profile compatibility,” pp282 – 286, ISIT, July 2006

[3].    M. Karczewicz, Y. Ye and I. Chong, Rate distortion optimized quantization, VCEG-AH21, Qualcomm,Jan. 2008