Each block is spatially and/or temporally predicted based on its causal neighbors. MediaKind has launched a new application paper titled ‘ Improving Video Compression with AI: Using Machine Learning to infer Coding Unit splitting for HEVC.’ This paper aims to demonstrate how it’s now possible to extend the use of ML techniques beyond previous methods, such as adjusting traditional encoder parameters (such as the search range for motion estimation) or guiding the choice of encoding modes, predictions, and in particular, block splitting.īy block splitting, we refer to how the input pictures in a frame are split into fixed-sized blocks. Using Machine Learning to infer Coding Unit splitting for HEVC It proves far more accurate than any human-defined heuristic or algorithm could. Doing so helps ensure the processing power is spent where it is most effective for that type of content. We can then use AI-driven compression to adaptively apply all available compute resource using the most beneficial balance between the possible encoding features. Through this analysis, we can generate AI-driven decisions within the encoder. It means we can better understand the dependency between those tools and the content. This makes it possible to identify complex data patterns that predict the optimal tools or encoding options to use in any given scenario. AI helps analyze decisions made by encoders using different toolsets. In video encoding, AI can be used to understand the characteristics of video content and then map how the encoders use their processing options to achieve the best results. But we have also driven innovations in other areas of compression delivery, including ML-guided CTU splitting, Constant Video Quality (CVQ) Adaptive Bitrate Streaming (ABR), and ML-Based video up-conversion. In recent months, I’ve described the benefits of MediaKind’s groundbreaking AI-based Compression Technology (ACT). We believe the use of ML and AI form the key to reducing infrastructure costs and improving the overall viewing experience. Optimized ML and AI can be used to help guide encoding tool selection for all use cases while guaranteeing the most efficient use of processing power. Over the past few years, our video research experts have invested large amounts into maximizing opportunities from recent developments in Artificial Intelligence (AI) and Machine Learning (ML), as part of our continued commitment to push the boundaries and deliver industry-leading video quality and hardware efficiency. The optimization of AI and Machine Learning As a result, some of the encoder designs that have been developed have delivered a compression efficiency that falls short of the codec’s overall potential. It means encoder developers often need to compromise on the encoding tools adopted to meet the computational resources budget for a given application. However, simultaneous increases in resolution and frame rates mean reliance upon hardware improvements is just not enough for certain applications. To date, constant hardware developments have partially fulfilled the ever-increasing requirements in processing power. Good broadcast video encoders need to make good decisions in real-time. In essence, the video encoder has one mission to optimize the video quality and bitrate. For content creators, video encoding can be the most critical part of a workflow. Thanks to the continued development of richer and ever more sophisticated sets of encoding tools, it has been possible to significantly increase the efficiency of each new generation of video codecs.
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