Your video upload now got even faster

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With our cloud encoding service, there’s no waiting for your video file to upload.

Telestream Cloud improves upload speed to the cloud encoding service with a new parallel multi-chunk uploader. The updated uploader utilizes available network bandwidth much more efficiently by sending multiple chunks at the same time.

The source file is divided into equal-length chunks, based on the file size. The uploader sends a number of chunks at the same time until all of them are uploaded and file is tested for integrity. If any chunks are missing (due to connection problems) they are re-sent and the upload is finished.

The new uploader is not only up to 5 times faster but also more reliable. Start your free trial of Telestream Cloud today or more information please call Contact inside sales at 530-264-7838.

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Telestream Debuts Telestream Cloud, High Quality SaaS (software-as-a-service) for Video Encoding in the Cloud

Nevada City, California, April 7, 2016At NAB 2016 on booth SL 4525, Telestream, a global market leader in video transcoding and workflow automation solutions, will debut Telestream Cloud, a video encoding SaaS (software as a service) ideally suited to meet the needs of video production and post production professionals.  Telestream Cloud features support for the popular Apple ProRes video format, and extends the reach of encoding capabilities to a wider audience seeking Telestream quality encoding, while offering cloud-encoding scalability for current users.

Telestream Cloud LogoTelestream’s Cloud service offers high quality video transcoding for all formats and codecs, and dynamically scales in response to customer requirements. The service utilizes an easy yet powerful API to automate and integrate into customer workflows. File transfer and encoding times are optimized from any location since Telestream Cloud leverages multiple cloud service providers including Google Cloud Platform, Amazon Web Services and Rackspace.

Telestream Introduces ProRes Support to Telestream Cloud

Telestream’s cloud-based transcoding service provides users with a fast, intuitive and highly cost-effective means to start producing ProRes output. For media that resides in the cloud and is destined for OTT distribution, Telestream Cloud takes a fraction of the time to commission and operate compared with an on-premises approach. Telestream Cloud pricing is structured as pay-as you-go monthly subscriptions for quick scaling as your workflow changes with no upfront expenses.

Telestream Cloud features certified ProRes presets to deliver high quality video files that conform to Apple standards and ensures trouble free, cost efficient post-production on Apple’s Final Cut Pro platform, as well as other popular editing software packages. Telestream Cloud supports Apple ProRes 422 Proxy, LT, SQ, HQ with resolutions up to 4K.

“Since its introduction, ProRes has revolutionized the post market, fundamentally changing the toolkit available to the widest audience of pro editors, and has become a standard mezzanine format for many media workflows,” commented Barbara DeHart, VP Desktop and Cloud Businesses at Telestream. “Now, Telestream is providing new impetus to this revolution, providing confidence of error free encoded files that unauthorized implementations might produce. Our elegant Cloud infrastructure offers all users a low-cost means of producing high quality ProRes files for their media workflows.”

Telestream Cloud Product Sheet

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Panda adds audio streams packing

Video files can contain more than just video, they usually have some kind of audio too, and sometimes subtitles or other data as well. Video processing is not an easy task, but one thing is certainly easier about it than about audio: there’s almost always just one video stream to worry about, but there can be multiple audio streams. Good, old stereo means having two audio streams, and nowadays we’ve gone much further than just two. We often have a small platoon of speakers around our couch, and each one of them might get its own audio stream.

A single container format (like MP4, MKV or AVI) can contain multiple audio streams. When transcoded, these streams can be left as they are, but sometimes you may need to map them to different speakers, merge or duplicate, even add muted streams. All these things have always been possible in Panda through raw FFmpeg commands, but recently we’ve decided to make it bit simpler.

Now you can decide if Panda should merge audio streams, with a single checkbox. The default behavior is to leave the streams mapping untouched. However, if you want to merge – Panda will intelligently pack them as audio channels into a single stream – 2 input streams will be packed as one stereo stream, 6 streams as one 5.1 surround streams, and so on.

Let us know if you have any questions.

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Panda’s new clothes

The neglected child…

New user interface for our web application has been long overdue. We focused so hard on improving Panda core features that we neglected the UI. A bit. Today is the day to make it up to you. New, updated and better Panda UI has arrived.

You’re probably very well aware that old UI was okay-ish but far from perfect. We decided to take it easy and choose evolution instead of revolution. Changing users habits and workflows is always sensitive and tricky business. That’s why we think it makes more sense to introduce changes gradually.

 

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Video list view

 

 

Our main goal for first roll out was to deliver cleaner, simpler UI that makes a better use of screen space. Based on your feedback we added small changes for those of you that transcode large volumes. We’ve added simplified list view for videos and profiles to make it easier to browse them. We unified the application behavior, to make sure configuration of key Panda features is always done the same way.

 

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Profiles list view

 

 

Front-end piece of the app is now based on well known and proven AngularJS framework.

What’s next?

Expect more changes in the coming weeks. We’re working on better, more detailed encoding analytics. That’s one of more requested improvements and we’re happy to oblige.

Current console will get overhaul as well to make it more useful. Brand new piece of Panda – Live Transcoding –  will be getting it’s own piece of UI (it’s in beta now). And of course, there’ll be a number of small tweaks improvements that at first may go unnoticed but will make your work with Panda both, easier and more fun.

We would love to hear what you think. What could have been done better? Did we miss something?

Stay tuned!

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On bears and snakes. Panda has updated Python library.

We usually don’t want to deal with complicated APIs, protocols and requests. Straightforward, clear way of doing things is preferred and it’s usually the best to hide raw communication and all the technical details under a simple interface. The structural organization of most successful systems is based on several layers of abstraction. The higher you are the less control over things you have but then such level of control is often dispensable in favor of simplicity.

Panda communicates with the rest of the world using several endpoints, related to particular entities it works with, like clouds, notifications and videos. Each of these endpoints can be reached using HTTP requests. Depending on their type (POST, GET, DELETE or PUT) and arguments various operations are executed. It could be modifying an existing profile, deleting a video or creating a new cloud. All these requests need proper timestamps and the right signature to pass the verification. To save you from managing it all on your own, several client libraries are used.

New Python library

We just wanted to let you know that Python library in Panda has been updated to make integrations much easier. So far it only offered basic functionality like signature generating. You still had to provide both, an endpoint location and a HTTP method to send a request, and then parse returned JSON data on your own. It’s no longer needed in the next version of the package, which introduces a new, simpler interface, based on the one provided by the Ruby language gem. Returned information is now stored in dictionary-like objects, which makes it easier to inspect. Also, you don’t have to input API endpoint locations and proper HTTP method types to interact with your data.

Resumable upload is there

Finally, a support for a resumable upload was added. If you send a file using a basic POST request, you don’t have a chance of resuming an upload in case of a connection failure. It is especially annoying if happens at the end of uploading large multimedia file. In such case, even through several gigabytes have already been sent, you have to start all over again. 

Panda offers another, much better approach, and allows you to create uploading session. The old version of the library only returned the endpoint address and left all the work up to you. The new one is now capable of managing the session using simple, easy to remember set of methods. You don’t have to calculate offsets and positions in a multimedia file anymore to ensure that it will be sent in one piece.

The backward compatibility with previous version is also preserved. If you prefer, you can still use the old way and call specific HTTP methods manually.

With the new library and thanks to power of Python you can easily write clear, robust, elegant and maintainable code. And that’s the fun part, isn’t it?

GitHub repo and examples

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XDCAM preset streamlined in Panda

XDCAM is a series of video formats that are widely used in the broadcasting industry, you might also know them as MXF. Sony introduced them back in 2003, and since then they’ve become quite popular among video professionals. It has always been possible to encode to XDCAMs in Panda through our raw encoding profiles, but we’ve decided to make it more streamlined. Oh, and, by the way, to make their quality possibly best in the industry.

And here it is, the new preset to create XDCAM profiles. Everything can be set up using Panda’s UI. Because XDCAMs only allow a predefined set of possible FPS values, we decided that it would be a good idea to always use our motion-compensated FPS conversion for XDCAM profiles (more on Google’s blog). If your input video’s frame rate doesn’t match that used by the XDCAM preset, or if it is progressive and you need interlaced outputs, the quality won’t degrade as much as it would without motion compensation. And that’s what gives our preset the best quality in the cloud video encoding industry.

 

Adding XDCAMs to your profiles is super easy now.
Adding XDCAMs to your profiles is super easy now.

Should you have any questions or suggestions regarding the new presets – just shoot us an email at team@pandastream.com.

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Encode Faster with Panda Multi-Core Encoders

We’ve been experimenting with ways to increase the speed of encoding times and we’ve developed a feature that allows you to turn the knob, slam the gas pedal, or push on the thrust lever. Multi-Core encoders.

Panda’s value proposition is buy an encoder and use it as much as you’d like. Our customers love that. If you have more volume of encoding jobs, you would add encoders. Adding encoders doesn’t increase the speed of jobs – you simply can encode more at once.

Video Encoding Kryptonite

Enter Panda Multi-Core. We think we have found the kryptonite to video encoding by enabling the use of multi-core encodings. With Panda Multi-Core, you now have the ability to turn on 2-32 cores on your video encoding processes.

At this point we are testing how much faster you can encode your videos using our most popular codecs, like WebM. We are still testing how fast we can increase the speed, but it is theoretically possible to increase encoding speed by 2x, 10x or even more.

Scenarios explained

Lets say you’re currently on our Crane plan, which is four encoders. That gives the capability of encoding four videos at the same time. With Panda Multi-Core you can turn up the speed on each of those encoders by as many cores as added.

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Those 4 encoders can tap into our multi-core system, and you can choose to have as many cores on each of those 4 encoders. For example, you can encode four videos at once, but with up to 32 cores each!

Its kinda like being in a water slide race against your best buds, but you just put on a jet pack.

Apply for Beta

Currently we are testing out multi-core encoders with a few of our customers and seeing great results. Do you want to reduce your encoding times by 2, 10 or even 32 times? Give us a shout!

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Announcing Panda Turbo

Add more encoders to help clear short term spikes or hit deadlines.

We have exciting news for Panda users who need to scale up their capacity to encode more files simultaneously for a short term period.

Turbo mode means that you don’t have to upgrade your monthly plan – you simply buy what you need – and the additional encoders will automatically expire after the time selected.

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When you log into your Panda account, on the Clouds page there is a link “Buy extra encoders for a defined time period”. You can then buy 4, 12, or 24 additional encoders for 12 to 48 hours.

Simply choose your two options and confirm. The additional charges will be added to your next monthly service charge and will be itemised in your receipt.

Our first step towards more flexible encoding.

This is our first step in introducing more flexibility into Panda based on discussions with our customers. We’re eager to hear your thoughts on Turbo mode, and suggestions for other improvements we can make to Panda in the future.

Any feedback good or bad please email info@copper.io

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