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Optimising the parameters of a video codec for a specific video clip has been shown to yield significant bitrate savings. In particular, per-clip optimisation of the Lagrangian multiplier in Rate controlled compression, has led to BD-Rate improvements of up to 20% using HEVC. Unfortunately, this was computationally expensive as it required multiple measurement of rate distortion curves which meant in excess of fifty video encodes were used to generate that level of savings. This work focuses on reducing the computational cost of repeated video encodes by using a lower resolution clip as a proxy. Features extracted from the low resolution clip are then used to learn the mapping to an optimal Lagrange Multiplier for the original resolution clip. In addition to reducing the computational cost and encode time by using lower resolution clips, we also investigate the use of older, but faster codecs such as H.264 to create proxies. This work shows the computational load is reduced by up to 22 times using 144p proxies, and more than 60% of the possible gain at the original resolution is achieved. Our tests are based on the YouTube UGC dataset, using the same computational platform; hence our results are based on a practical instance of the adaptive bitrate encoding problem. Further improvements are possible, by optimising the placement and sparsity of operating points required for the rate distortion curves. Our contribution is to improve the computational cost of per clip optimisation with the Lagrangian multiplier, while maintaining BD-Rate improvement.
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