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implement much more demanding video quality measurement methods than SSIM with a fraction of the CPU power available.

n Step 2: The amount of the video compression is determined by the rate controller based upon a model that predicts the number of bits that the compression will create, given the measured video characteristics. n Step 3: How much video compression is applied is a good predictor of the compressed video quality. However, this prediction is inaccurate. n Step 4: Adding a measurement for video quality accuracy brings huge value to the equation. The video encoder is tuned to avoid video quality problems. Without accurate feedback, it must rely on safety margins to avoid generating bad video quality. These margins are relatively large. Conclusion In recent years, video encoders have transitioned from hardware to software due to the availability of high-performance CPUs that enable more flexibility in algorithm development at a cost of ownership which is lower than hardware-based solutions, accelerating the rate of video compression improvement. The use of advancements in HVS research described in this article are a result of such a technology shift. It is now possible to

At Harmonic, we’ve identified a new way to determine the target video rates for the different video profiles in an ABR deployment. Our video quality measurement approach can also be applied to broadcast services. Extensive testing shows that providing operators with a scalable real-time video compression optimisation solution that leverages the HVS is proved to boost compression efficiency by 50 per cent, while preserving video quality targets. When this video optimisation solution is used in the real world, viewers can watch pristine video on their device of choice — even over constrained OTT/ABR networks. As video quality goes up, subscribers are happier and spend more time watching, improving monetisation opportunities for content providers and operators.

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Vol. 38 No. 4 - November 2016 Issue