The world of broadcast and production can be a minefield of contradictory challenges. Reduce bit rates, but maintain visual quality; apply precise subtitles, but broadcast in real time; create highlight content, but transmit it immediately after the main event. According to Futuresource Consulting, these and many more headaches can be alleviated by the rise of Artificial Intelligence (AI) and machine learning technologies.
“Machine learning is beginning to impact on the video market, unlocking a range of opportunities for the industry,” says Simon Forrest, Principal Technology Analyst at Futuresource Consulting. “One of the most notable, but perhaps lesser-known areas are video encoding technology. Machine learning techniques allow encoders to optimise video encode parameters on a scene-by-scene basis. Meanwhile, the results are fed back into the system to enhance future video encoding; this feedback loop ensures machine learning applies better encode parameters in subsequent sessions.
“Over time, the system accelerates towards the optimum compression for a given scene. This leads to significant cost savings in network bandwidth and delivery, and the more efficiently a broadcaster or OTT service provider uses bandwidth, the more profit becomes available to them.”
Looking to closed captioning or subtitles, this is another area where machine learning techniques can be applied, according to Forrest. Using algorithms that have been guided through massive language datasets, speech can be translated into text in real time and automatically applied to broadcast assets. In addition, AI can also be engineered to loosely identify the context of the speech. Moving forward, future iterations may use intonation and inflexion to further improve accuracy rates.
When it comes to highlight content or showreels, companies like Aspera are already employing machine learning techniques to automatically search video for specific content, both audio and visual. Once indexed, portions of the video can be stitched together to produce highlights which are made available to programme editors. For example, goals during a football game may be found by identifying video sequences where the goalmouth is present in the scene and the crowd is cheering.
“It’s clear that machine learning and AI technology is being actively pursued in the video industry, and companies are already reaping the benefits,” says Forrest. “However, AI is still a nascent technology with many developments yet to come. Machine learning and AI have been limited to running on servers within the cloud. This is beginning to change, with semiconductor vendors now building neural network accelerators (NNAs) into silicon chips. This will allow elements of AI to run at the edge, on consumer electronics devices, and it will be possible to identify users locally, either vocally through voice fingerprint or visually via camera. No data needs to leave the device itself, leading to improved security and increased privacy for the consumer. Companies like Synaptics are already developing the next generation of set-top boxes (STB) chips, capable of AI at the edge, so the future is already beginning to reveal itself.”
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