well as a team doing optimisation for GPU and CPU platforms. You need the right people coming together.” Despite the company’s academic beginnings, the team took care to keep the practicalities of electronics in mind. Lorent continues: “We learnt a lot from JPEG 2000, which was more a codec created by the research community and less by people connected to the industry, the markets and similar areas.” Intopix began developing Tico just a few years later, in the early 2010s. It quickly became clear there was room in the world for a very lightweight codec, and the design was rapidly adopted as a SMPTE standard. “Intopix has always been an active contributor on the JPEG committee,” Lorent points out. “You have two main groups – JPEG and MPEG – and at JPEG, we discussed it with the chairman at that time. He questioned, ‘Why is Intopix not launching a standard for lightweight technologies?’” The result was JPEG XS, which Lorent describes as ‘an evolution of Tico.’ The benefits of Tico’s tininess are not only in power consumption but also latency, as Lorent describes it. “As an example, with Tico Raw, if I have a quad-core CPU then I can encode or decode 8K at 60fps, at 10:1, 12:1, the sort of compression ratio you find with ProRes or other codecs,” he states. Meanwhile, the advantage of the codec’s computational simplicity is that it can be implemented at the very start of an imaging chain, with the potential for improved efficiency in every downstream component – as well as other enhancements in unexpected ways.
“Intopix innovates in sensor compression,” Lorent remarks. “We license Tico Raw to Nikon. By integrating Tico Raw compression into a sensor with four parallel interfaces connecting to the processor, we can reduce the number of interfaces from four to just one.” With the lion’s share of power consumption in many sensors created by the need to drive some very fast external data interfaces, power consumption falls and the hardware platform can be simpler and cheaper. Beyond photography, film and TV, new compression ideas have relevance to AI and machine-learning applications. Systems may even be able to work on the compressed data, removing the need to unpack it at all. “Intopix is active and also involved in the automotive market,” Lorent adds. “Clearly, the amount of data is huge if you consider the number of video sensors. We believe more and more processes will occur in the compressed (or partially compressed) domain for machine learning – but the road is still long.” With many fields still clinging to uncompressed video because of latency and quality, Lorent accepts that ‘people need to change their minds, and the only reason they would change is if they could say nothing against the latency or quality of a codec.’ “If you want to replace uncompressed, then complexity and power consumption are key. We start from the people who don’t want to hear about compression. If we can create a codec which is low power, low latency, not even millisecond but microsecond latency and without loss in quality, we’ll be fine.”
MORE PROCESSES WILL OCCUR IN THE COMPRESSED, OR PARTIALLY COMPRESSED, DOMAIN FOR MACHINE LEARNING feedmagazine.tv
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