2019年情境感知编码买家指南

There are myriad ways to approach the problem of lowering overall video delivery costs, but one fundamental area has eluded the overall value chain—encoding a video, 甚至是视频的一部分, against the content (what’s in the video) or the context (where the video will be consumed).

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2018年是上下文感知编码(CAE)成为主流的一年 流媒体 报道了该领域的一些进展, including presentations about major on-demand video content libraries being re-encoded using discrete parameters for an entire season of an episodic show. These title-specific encoding parameters are used by the likes of Netflix and others to optimize quality on a general level, on the assumption that an episodic television program’s dozen or so individual shows in a given year will have the same “look and feel” across the entire season.

很有可能, given the advances in machine learning (sometimes also referred to as artificial intelligence or AI) as well as the move toward mass parallelization of on-demand encoding using cloud-based transcoding infrastructures, 2019年将是上下文感知编码达到临界质量的一年.

在今年的其他地方 流媒体行业资料手册, 有一个关于人工智能和机器学习现状的广泛讨论, but this Buyers’ Guide will focus on two practical areas to consider when purchasing a product or service offering either context- or content-aware encoding features.

背景是什么??

如果编码是为了节省总带宽, 一个主要因素是内容被消费的地点. The traditional approach to encoding requires creating typical settings for each video in a library.

在视频将被流式传输到多个设备的情况下(例如.g., mobile phones on cellular data networks as well as set-top boxes that use a wired Ethernet connection), 必须有多个编码参数, 每一种类型的设备对应一个将被传送的视频.

这种对上下文的意识导致了通常被称为引渡-比特率的离散组合, 编解码器, 帧速率, and image resolutions that offer bandwidth-saving results for a particular device in a particular setting. 例如, a mobile device using cellular data will more than likely stumble over high-bitrate encodings, 而同样的移动设备在Wi-Fi上则不会. This rudimentary context-aware encoding aims at specific bandwidth savings in discrete content-device-network combinations, generating a matrix of 引渡s against which to encode a given video library.

Even if the approach is fine-tuned at an episodic show’s per-season level, 虽然, it is still a best-effort approach more akin to using a bludgeon rather than a scalpel for surgery. 上下文感知编码的新方法, 由Brightcove和其他公司在2018年推出, 设想在每个节目的基础上微调参数, 甚至在更激进的解决方案中，基于每个场景的编码.

在一个我在Brightcove的建议下撰写的白皮书, the company says the use of context-aware encoding “potentially offers its users the same bandwidth-savings benefits that dedicated compression teams like Netflix have enjoyed for some time now in their own per-title and per-scene encoding solutions.”

网络要素

Brightcove’s CAE solution focuses on an often-overlooked ingredient to proper context-aware encoding—the network on which content will be delivered.

It makes sense that the company would add the networking ingredient to the overall encoding recipe, since one of the promises of context-aware encoding is to generate fine-tuned 引渡s. 像这样, 在Brightcove溶液中, “视频资产被分析为最佳比特率, 考虑到预期的传送设备或网络的能力.”

没有网络环境, 以最佳比特率正确编码视频是, 在最好的情况下, 盲目的赌博.

什么是在它的CDN或OVP?

A question I’m often asked around context-aware encoding centers on the motivation for companies— from content delivery networks (CDNs) to online video platforms (OVPs)—to offer a bandwidth-saving service. The trend seems to be to offer flat-rate packages for enterprise video customers, with pricing based around numbers of hours of content delivered rather than overall bandwidth. 像这样, it’s in the best interest of the CDN or OVP to figure out ways to reduce overall bandwidth, and CAE is just one of multiple viable ways to deliver those overall bandwidth savings.

考虑到H.264—also referred to as MPEG-4 Part 10 or Advanced Video Coding (AVC)— the use of context-based encoding is also one of only a few ways that bandwidth can be saved while still maintaining image quality.

关于上下文感知编码的好消息是, 不管编解码器是什么, there will always be a need to balance the content-device-network delivery matrix. For that reason, CAE will continue to thrive even with the ascendancy of H.265(也称为高效视频编码或HEVC).

对速度的需求

One key differentiator between services offering a context-aware component is the speed at which they create these 引渡s and encoding parameters. 一些解决方案使用多个分析通道, while others generate the parameters off a single video asset and then apply those 引渡 parameters across the whole season of an episodic show.

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