As the digital landscape continues to grow, it’s no surprise that the demand for a high-quality and reliable streaming video experience on mobile devices is increasing. In fact, Cisco reported that by 2019, video will represent 80% of all global consumer Internet traffic. This means, once every second, nearly 1 million minutes of video content will cross the network. And more than 50% of this video traffic will traverse content delivery networks (CDNs).
Given these trends, it’s more important than ever for video content distributors to pursue more efficient methods of encoding their video so they can adapt to the rapidly changing market, and this is where content-adaptive optimization can provide a huge benefit.
Recently popularized by Netflix and Google, content-adaptive encoding is the idea that not all videos are created equal in terms of their encoding requirements. I recently wrote a blog post on the subject, you can read it here:
The concept is easy to explain but difficult to execute.
Not every scene is created equal
Content-adaptive media optimization complements the encoding process by driving the encoder to the lowest bitrate possible based on the needs of the content, and not a fixed, target bitrate (as seen in a traditional encoding process).
This means that a content-adaptive solution is able to optimize more efficiently by analyzing already-encoded video on a frame-by-frame and scene-by-scene level, detecting areas of the video that can be further compressed without losing perceptual quality (i.e. slow motion scenes, smooth surfaces).
Provided these calculations are performed at the frame level with an optimizer that contains a closed loop perceptual quality measure, the output can be guaranteed to be the highest quality at the lowest bitrate possible.
Content-adaptive secret sauce
Reducing bitrate while maintaining perceptual quality may sound simple. Truth is, it takes years of intensive research that extends from the innermost workings of encoding science all the way to the study of block based encoding artifacts. This is work that Beamr has undertook since 2009 and it is the reason why our method of reducing bitrate has been recognized by the industry as the highest quality and safest. The Beamr perceptual quality measure is so highly correlated to human vision that viewers cannot tell the difference between a source video file and an optimized one, provided Beamr Video was used in the process. Video samples can be seen on our homepage.
The magic of Beamr Video is that we apply the optimization process in a closed loop, making it possible to determine the subjective quality level of both input and output video streams. And as a result the video encoding process is controlled by setting the compression parameters per video frame. This method guarantees the optimal bitrate for any type of content. For example, high motion content and highly detailed textures will receive more bits, whereas low motion content with smoother textures receive less bits.
Flexibility is key to the content-adaptive media optimization technology, and this is what enables finding the best bitrate-quality balance. From a business perspective, the result is smaller files with the same (or better) quality, requiring less storage and enhancing delivery of high quality video over congested networks.
How encoding and optimization work together
Since the content-adaptive optimization process is applied to files that have already been encoded, by combining the industry leading H.264 and HEVC encoder with the best optimization solution (Beamr Video), the market will benefit by receiving the highest quality video at the lowest possible bitrate. Which as a result, will allow content providers to improve the end-user experience with high quality video, while meeting the growing network constraints due to increased mobile consumption and general Internet congestion.
To dive deeper into the subject, we invite you to download The Case for Content-Adaptive Optimization whitepaper.