In our previous post, we talked about the importance of choosing a protocol that supports multiple codecs so that you have the best end-user experience possible, while also having the optimal CPU load and bandwidth usage. Choosing a multi-codec protocol, such as PCoIP vs a single-codec protocol like H.264, recognizes that your experience on your desktop is dynamic and that your protocol should be dynamic as well.
When comparing protocols, however, it’s not just about multiple codecs. One of the other important distinctions between PCoIP and other protocols such as VMware Blast Extreme, which is based upon the H.264 codec, is the concept of lossless support.
What Does Lossless Mean?
In this context, we are talking about the concept of lossless image compression. Lossless compression refers to the ability for an image to be compressed and decompressed again without any loss of data.
From an end-user perspective, lossless support means that an image loads cleanly, with no loss in quality, or that text is clear and easy to read.
By comparison, lossy compression algorithms reduce file sizes by intentionally throwing away some of the data, where the trade-off is reduced quality. The more aggressive the compression, the more likely the user will notice a degraded experience.
PCoIP codecs are optimized for truly lossless support, which is important in many applications such as financial services, media and entertainment, CAD, healthcare, GIS, and oil and gas. Referencing back to our discussion on multiple codecs, Teradici’s PCoIP protocol dynamically chooses the appropriate codec to apply, depending on the type of content. What this means is that we are able to use the right codec in the right situation, so we don’t need to sacrifice quality to achieve optimal results. Most text and simple graphics are sent lossless, right away.
Microsoft and Adobe go to great lengths to optimize text for LCD screens using RGB sub pixel ClearType and CoolType technologies (as shown above). Research has shown that optimized ClearType and CoolType rendered text improved word recognition, reading speed, comprehension, and even reduced mental fatigue.
If you rely on a protocol based on a single codec, you are going to notice the difference when it comes to text which is not lossless. Text and images are filled with sharp edges and high contrast, so any loss in quality such as spurious pixel renderings or inaccurate color reproduction is going to impact the end user.
Let’s look at an example of Adobe CoolType text compressed using a popular H.264 codec:
Can you imagine staring at blurry text all day?
Not only does it eliminate the benefits achieved by ClearType and CoolType, H.264 requires 3x the bandwidth of PCoIP to deliver this inferior result.
For other types of content, human perception is such that lossy compression can be used with little or no perceptual difference. This is particularly true for natural images that are constantly changing with video being the best example.
Let’s look at the most common video codecs, like H.264. The first compression step they use is to perform a color space conversion called YUV 4:2:0 which results in a 2:1 compression, but throws away 75% of the color information. This loss of color data is irreversible.
Color Space Conversion:
Now, you may ask, is this difference even noticeable?
In video, we are less likely to notice the loss of color because our eyes are more sensitive to Luminance (Y) vs Chroma (U/V) and because of the way our brain averages the results of images that are moving. This is why a codec like H.264 is said to be optimized for video, because even if it’s applied aggressively, you’re still not likely to notice the loss of image quality in a video scenario.
The same does not hold true for text or other computer-generated images which are static and have sharp color transitions. The lossy PCoIP codecs use a fully, reversible color space conversion with a layered design that enables an efficient build-to-lossless capability that does not require sending the content more than once and provides higher quality computer-generated images as shown here:
So, is lossless support important to you?