In the ongoing battle to find the minimum bitrate for good looking video, automation is key to achieving this quickly and cheaply. However, metrics like PSNR don’t always give the best answers meaning that eyes are still better the job than silicon.
In this talk from the Demuxed conference, Intel’s Vasavee Vijayaraghavan shows us examples of computer analysis failing to identify lowest bitrate leaving the encoder spending many megabits encoding video so that it looks imperceptibly better. Further more it’s clear that MOS – the Mean Opinion Score – which has a well defined protocol behind it continues to produce the best results, though setting up and co-ordinating takes orders of magnitude more time and money.
Vasavee shows how she’s managed to develop a hybrid workflow which combines metrics and MOS scores to get much of the benefit of computer-generated metrics fed into the manual MOS process. This allows a much more targeted subjective perceptual quality MOS process thereby speeding up the whole process but still getting that human touch where it’s most valuable.
MUX is a very pro-active company pushing forward streaming technology. At NAB 2019 they have announced Audience Adaptive Encoding which is offers encodes tailored to both your content but also the typical bitrate of your viewing demographic. Underpinning this technology is machine learning and their Per-title encoding technology which was released last year.
This talk with Nick Chadwick looks at what per-title encoding is, how you can work out which resolutions and bitrates to encode at and how to deliver this as a useful product.
Nick takes some time to explain MUX’s ‘convex hulls’ which give a shape to the content’s performance at different bitrates and helps visualise the optimum encoding parameters the content. Moreover we see that using this technique, we see some surprising circumstances when it makes sense to start at high resolutions, even for low bitrates.
Looking then at how to actually work out on a title-by-title basis, Nick explains the pros and cons of the different approaches going on to explain how MUX used machine learning to generate the model they created to make this work.
Finishing off with an extensive Q&A, this talk is a great overview on how to pick great encoding parameters, manually or otherwise.
In this webcast, AWS Elemental dives into a topic other companies don’t discuss: The video encoding techniques some vendors use to sway judgment during proof-of-concept demos.
Improve your discerning eye and your analysis acumen, and obtain essential knowledge:
Methods and best practices for evaluating video quality
Examples of scenes which look totally different, but have the same metrics score
Important considerations for accurate evaluation of video results
Subjective vs. Objective results
How encoder settings affect output and how you can fine tune them for the best results
What is Adaptive Quantization and how can it improve your video output quality
Encoding techniques that impress in shootouts but might not be practical in production
Video encoding comparisons should inform, not fool. Learn what you need to know, before you start the process.
Solutions Marketing Manager for Compression, AWS ElementalDan Gehred, Solutions Marketing Manager for Compression at AWS Elemental, is responsible for product marketing for all compression software products. Dan has over 15 years of experience building and marketing digital media applications.
Sr. Product Manager Compression – VOD, AWS ElementalWith over 30 years of industry experience in the international media marketplace, Dan is passionate about technology, developing new standards, promoting positive change and business approaches to enhanced profitability.