39 whether in the air or on the ground, require that the operator can see as much of the surrounding environment as possible. As the camera sensors are moving to 4K resolution, so the demand on the encoder to deliver the video stream in real time over an existing radio link without losing or dropping frames is a major challenge. This drive to squeeze more video over wireless links has driven the development of the latest standard, H.266 or Versatile Video Coding (VVC), approved last year. Block-based encoding works well on scenes where there is limited change from frame to frame, which is suitable for uncrewed control systems, but dynamic scenes with a lot of complex movements can overwhelm the encoder or the wireless link. However, this is currently implemented as software in power-hungry dual-core processors or as hardware in expensive FPGA chips, which again can be powerhungry. These devices can be used in larger UAVs or driverless cars, and the current devices combine four processor cores with the programmable logic fabric to reduce costs. In previous generations of the technology, the encoding and decoding functions have gone on to be implemented directly in silicon to provide standard conversion with low power and lower cost. For uncrewed systems however, that cycle of development for lower cost and lower power has slowed, and there is another major technology factor that is set to change the direction of development. Machine learning (ML), or AI, algorithms are now being applied to the video before encoding to reduce the amount of data that needs to be processed, and even to use different techniques for encoding the images, such as describing a scene in text and using generative AI to regenerate the scene at the other end. ML is considered an essential new tool in the progress towards future codecs. Its techniques are under scrutiny by the major video standards organisations, including the MPEG JVET Ad hoc Group 11 (AhG11). This group, part of the same organisation that developed the previous compression technologies, is looking at new standards with AI at the centre of the coding scheme and examining related ML techniques that can be used to boost existing encoders. This work has been named Neural Network Video Coding, to create an AI-based codec before the end of the decade. This is looking at the use of ML for encoding more dynamic scenes, and could uncover entirely new methods for coding and transmission. In the short term, ML is being used to enhance existing coding tools, allow encoders to work on partial frames, and to Video encoding | Focus An H.265 video encoder card with low power and size (Courtesy of Antrica) Relative bit rates of video compression technologies (Courtesy of Interdigital) Uncrewed Systems Technology | October/November 2023
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