Both demand and accessibility for image and video media services is increasing day-by-day. Several IPTV/OTT based media services are becoming available through the internet. Media content quality is an essential topic of concern as there is still a lot of low-quality media content that needs to be enhanced. Degradation of image/ video content is mainly due to the quantization during the lengthy coding process. This degradation becomes significantly worse as customers are located where the transmission bandwidth becomes narrower because the bitrate for the encoded media contents' bitstream becomes lower in this environment. Another degradation case is when the spatial resolution for the delivered image/video is too small for customers to watch with their FHD or 4-K display.

As I reflect on cloud computing and the media industry since last year's NAB, I see two emerging trends. First, content creators and broadcasters such as Rakuten, RTL, and Al Jazeera are increasingly using the global reach, hybrid model, and elastic scale of Azure to create, manage, and distribute their content. Second, AI-powered tools for extracting insights from content are becoming an integral part of the content creation, management and distribution workflows with customers such as Endemol Shine Group, and Zone TV. Therefore, at this year's NAB, we are focused on helping you modernize your media workflows, so you can get the best of cloud computing and AI. We made a number of investments to enable better content production workflows in Azure, including the recent acquisition of Avere Systems.

Nearly nine in 10 Americans (85%) say they currently use at least one of six devices, programs or services that feature elements of artificial intelligence (AI). These data come from a Northeastern University/Gallup survey of Americans' attitudes toward artificial intelligence (AI) and its effect on their lives and work. The mail survey of 3,297 U.S. adults was conducted Sept. 15-Oct. The study found 79% of Americans saying AI has had a very or mostly positive impact on their lives so far. The percentage of Americans using different consumer AI products may be at least partly related to the length of time these products have been on the market.

Azure Media Services, Microsoft's collection of cloud-based tools for video workflows, is about to get a lot smarter. As the company announced at the annual NAB show in Las Vegas today, Media Services will now make use of some of the tools Microsoft developed for its machine learning services for video, as well. This means Media Services can now automatically select the most interesting snippets from a source video, for example, to give you a quick summary of what the full video looks like. In addition, Microsoft is building face detection into these tools and the company is including its ability to detect people's emotions (something the company's Cognitive Services already do for still images). Using this, you could easily see how people reacted to a speech at an event, for example.

At the annual NAB (National Association of Broadcasters) show in Las Vegas, Microsoft announced that Azure Media Services will be receiving a host of new features and will even be taking advantage of its machine learning services. Azure Media Services is a collection of cloud-based tools for video workflows and the company intends to make these tools smarter to help users create premium video content more easily. Microsoft will be adding face detection to Media Services through its Face API. The company has decided to go a step further though and will also use the Emotion API from Cognitive Services to detect the emotions of the people who appear in a video. This feature will be useful when it comes to discerning the level of interest of an audience during debates or keynotes.

Microsoft is notching up its Azure Media services platform by a couple of notches. The company is now going to implement its machine learning tools into its collection of cloud-based tools for video workflows. Now, you may wonder at the apparent non-existence of a relation between videos and machine learning. Machine learning after all, is used for data analysis. It can't be used with videos, right?