Abstract
In this paper, we present a hybrid video compression technique that combines the advantages of residual coding techniques found in traditional DCT-based video compression and learning-based video frame interpolation to reduce the amount of residual data that needs to be compressed. Learning-based frame interpolation techniques use machine learning algorithms to predict frames but have difficulty with uncovered areas and non-linear motion. This approach uses DCT-based residual coding only on areas that are difficult for video interpolation and provides tunable compression for such areas through an adaptive selection of data to be encoded. Experimental data for both PSNR and the newer video multi-method assessment fusion (VMAF) metrics are provided. Our results show that we can reduce the amount of data required to represent a video stream compared with traditional video coding while outperforming video frame interpolation techniques in quality.
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