VP9协议文档是WebM项目的一部分，由Google开发，旨在提供一种高效、开源的视频编码标准，用于在网络上进行高质量的视频传输。VP9是继VP8之后的下一代视频编码技术，它在压缩效率上有了显著提升，从而可以降低带宽需求，提高视频流的播放质量。以下是关于VP9协议的一些关键知识点： 1. **编码架构**： - VP9采用了基于宏块的编码结构，将视频帧划分为不同大小的块进行处理，以适应不同的图像内容。 - 使用帧内预测（Intra Prediction）和帧间预测（Inter Prediction）来减少冗余信息，通过比较当前块与相邻块或先前帧的相似性进行预测。 2. **熵编码**： - VP9使用了自适应熵编码，包括上下文自适应二进制算术编码（Context-Adaptive Binary Arithmetic Coding, CABAC）和上下文自适应变量长度编码（Context-Adaptive Variable Length Coding, CAVLC）。 - 这种编码方式可以根据已解码的信息调整编码概率模型，以进一步优化压缩效率。 3. **颜色空间和分辨率**： - VP9支持多种颜色空间，包括YUV 4:2:0、4:2:2和4:4:4，其中4:4:4保留了完整的色度信息。 - 它还支持不同分辨率的编码，包括多分辨率编码，允许在一个视频流中同时存在不同分辨率的帧，适用于自适应流媒体。 4. **分层编码**： - VP9支持时间分层（Temporal Layers），这使得视频编码器可以根据网络条件动态调整视频质量，而不会丢失同步。 - 也支持空间分层（Spatial Layers），允许编码多个独立的图像平面，实现多视点视频编码或自适应流媒体。 5. **环路滤波与去块效应**： - 副本滤波（Loop Filter）是VP9中的一个重要组成部分，用于减少编码过程中的块效应和运动模糊。 - 该滤波器在解码后应用，可以平滑块边界，提高图像质量。 6. **自适应刷新**： - VP9引入了自适应刷新（Keyframe Placement）机制，允许编码器根据需要灵活地插入关键帧（I帧），有助于错误恢复和流媒体适应。 7. **多参考帧**： - VP9支持多达4个参考帧，这增强了帧间预测的能力，可以更精确地预测运动补偿，减少错误传播。 8. **ROI（Region of Interest）编码**： - ROI编码允许对视频中的特定区域进行优先编码，如对人物或动作丰富的区域分配更高的比特率，以提高视觉质量。 9. **增强的运动补偿**： - VP9使用更复杂的运动估计算法，包括1/4和1/8像素精度的运动矢量，提高运动补偿的准确性和效率。 10. **错误隐藏和恢复**： - 在网络不稳定时，VP9有错误检测和隐藏机制，比如使用恢复点（ Recovery Points）和错误隐藏策略，确保视频播放的连贯性。 通过VP999999.pdf文档，读者可以深入理解VP9的位流结构、解码过程、编码优化策略以及其实现高效视频压缩的细节。这份文档对于开发者、研究人员和视频编码爱好者来说，是一份非常宝贵的参考资料。

VideoProAnalyzerEvaluation工具，该工具是专门针对最新的H.265和VP9的

完整的H263和H264和H265和MPEG4和vp8和vp9测试影片

4k 测试视频，一个是vp8 编码，一个是vp9编码的

H_265_HEVC_VP9_H_264编码算法比较及性能测试分析.docx

官方文档，与大家共享，有经验者请留下联系方式，以后一起探讨

跨平台视频编码gui 编码视频很慢。 qencoder可以使其快速！ 最高效的av1和vp9 / vp8编码器无法在许多CPU内核上很好地扩展。 通过智能地将视频分成多个块，qencoder可以让您比使用svt在更少的时间内编码更好的视频。 qencoder被激发，并会使用代码 ，同时提供Windows和Linux更熟悉的GUI体验。 简单易用 您无需对视频的工作原理有深入的了解即可利用qencoder。 qencoder具有非常易于使用和强大的预设功能，适合每个人。 对于需要它的人功能强大 qencoder具有许多有用的功能，这使其成为功能强大的工具。 通过基于场景的拆分，qencoder是第一个利用具有数百个内核的系统的GUI。 通过在适当的时候进行分割，qencoder可确保您的视频不会因不需要的关键帧而产生任何开销。 它也是第一个能够增强黑暗场景的图形用户界面。 允许您使用

VP9相比VP8有着很多的提升。在比特率方面，VP9比VP8提高2倍图像画质，H265的画质也比H264高2倍。VP9一大的优势是没有版税。和H.264和H.265不同，它免费进行使用。

世界上最快的VP9视频解码器 As before , I was very excited when Google released VP9 – for one, because I was one of the people involved in creating it back when I worked for Google (I no longer do). How good is it, and how much better can it be? To evaluate that question, Clément Bœsch and I set out to write a VP9 decoder from scratch for FFmpeg. The goals never changed from the original ffvp8 situation (community-developed, fast, free from the beginning). We also wanted to answer new questions: how does a well-written decoder compare, speed-wise, with a well-written decoder for other codecs? TLDR (see rest of post for details): as a codec, VP9 is quite impressive – it beats x264 in many cases. However, the encoder is slow, very slow. At higher speed settings, the quality gain melts away. This seems to be similar to what people report about HEVC (using e.g. x265 as an encoder). single-threaded decoding speed of libvpx isn’t great. FFvp9 beats it by 25-50% on a variety of machines. FFvp9 is somewhat slower than ffvp8, and somewhat faster than ffh264 decoding speed (for files encoded to matching SSIM scores). Multi-threading performance in libvpx is deplorable, it gains virtually nothing from its loopfilter-mt algorithm. FFvp9 multi-threading gains nearly as much as ffh264/ffvp8 multithreading, but there’s a cap (material-, settings- and resolution-dependent, we found it to be around 3 threads in one of our clips although it’s typically higher) after which further threads don’t cause any more gain. The codec itself To start, we did some tests on the encoder itself. The direct goal here was to identify bitrates at which encodings would give matching SSIM-scores so we could do same-quality decoder performance measurements. However, as such, it also allows us to compare encoder performance in itself. We used settings very close to recommended settings forVP8,VP9andx264, optimized for SSIM as a metric. As source clips, we chose Sintel (1920×1080 CGI content, source ), a 2-minute clip from Tears of Steel (1920×800 cinematic content, source ), and a 3-minute clip from Enter the Void (1920×818 high-grain/noise content,screenshot). For each, we encoded at various bitrates and plotted effective bitrate versus SSIM . sintel_ssimtos_ssimetv_ssim You’ll notice that in most cases, VP9 can indeed beat x264, but, there’s some big caveats: VP9 encoding (using libvpx) is horrendously slow – like, 50x slower than VP8/x264 encoding. This means that encoding a 3-minute 1080p clip takes several days on a high-end machine. Higher –cpu-used=X parameters make the quality gains melt away. libvpx’ VP9 encodes miss the target bitrates by a long shot (100% off) for the ETV clip, possibly because of our use of –aq-mode=1. libvpx tends to slowly crumble at higher bitrates for hard content – again, look at the ETV clip, where x264 shows some serious mature killer instinct at the high bitrate end of things. Overall, these results are promising, although the lack-of-speed is a serious issue. Decoder performance For decoding performance measurements, we chose (Sintel)500 (VP9), 1200 (VP8) and 700 (x264) kbps (SSIM=19.8); Tears of Steel4.0 (VP9), 7.9 (VP8) and 6.3 (x264) mbps (SSIM=19.2); and Enter the Void 9.7 (VP9), 16.6 (VP8) and 10.7 (x264) mbps (SSIM=16.2). We used FFmpeg to decode each of these files, either using the built-in decoder (to compare between codecs), or using libvpx-vp9 (to compare ffvp9 versus libvpx). Decoding time was measured in seconds using “time ffmpeg -threads 1 [-c:v libvpx-vp9] -i $file -f null -v 0 -nostats – 2>&1 | grep user”, with this FFmpeg and this libvpx revision (downloaded on Feb 20th, 2014). sintel_archs tos_archsetv_archs A few notes on ffvp9 vs. libvpx-vp9 performance: ffvp9 beats libvpx consistently by 25-50%. In practice, this means that typical middle- to high-end hardware will be able to playback 4K content using ffvp9, but not using libvpx. Low-end hardware will struggle to playback even 720p content using libvpx (but do so fine using ffvp9). on Haswell, the difference is significantly smaller than on sandybridge, likely because libvpx has some AVX2 optimizations (e.g. for MC and loop filtering), whereas ffvp9 doesn’t have that yet; this means this difference might grow over time as ffvp9 gets AVX2 optimizations also. on the Atom, the differences are significantly smaller than on other systems; the reason for this is likely that we haven’t done any significant work on Atom-performance yet. Atom has unusually large latencies between GPRs and XMM registers, which means you need to take special care in ordering your instructions to prevent unnecessary halts – we haven’t done anything in that area yet (for ffvp9). Some users may find that ffvp9 is a lot slower than advertised on 32bit; this is correct, most of our SIMD only works on 64bit machines. If you have 32bit software, port it to 64bit. Can’t port it? Ditch it. Nobody owns 32bit x86 hardware anymore these days. So how does VP9 decoding performance compare to that of other codecs? There’s basically two ways to measure this: same-bitrate (e.g. a 500kbps VP8 file vs. a 500kbps VP9 file, where the VP9 file likely looks much better), or same-quality (e.g. a VP8 file with SSIM=19.2 vs. a VP9 file with SSIM=19.2, where the VP9 file likely has a much lower bitrate). We did same-quality measurements, and found: ffvp9 tends to beat ffh264 by a tiny bit (10%), except on Atom (which is likely because ffh264 has received more Atom-specific attention than ffvp9). ffvp9 tends to be quite a bit slower than ffvp8 (15%), although the massive bitrate differences in Enter the Void actually makes it win for that clip (by about 15%, except on Atom). Given that Google promised VP9 would be no more than 40% more complex than VP8, it seems they kept that promise. we did some same-bitrate comparisons, and found that x264 and ffvp9 are essentially identical in that scenario (with x264 having slightly lower SSIM scores); vp8 tends to be about 50% faster, but looks significantly worse. Multithreading One of the killer-features in FFmpeg is frame-level multithreading, which allows multiple cores to decode different video frames in parallel. Libvpx also supports multithreading. So which is better?

vp9,vp8编码器，版本1.8.1，msys2编译。vp9是目前浏览器上能用的压缩率最高的编码器。