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Modern video codecs have many compression-tuning parameters from which numerous configurations (presets) can be constructed. The large number of presets complicates the search for one that delivers optimal encoding time, quality, and compressed-video size. This paper presents a machine-learning-based method that helps to solve this problem. We applied the method to the x264 video codec: it searches for optimal presets that demonstrate 9-20% bitrate savings relative to standard x264 presets with comparable compressed-video quality and encoding time.

This paper proposes extensions of CALIC for lossless compression of light field (LF) images. The overall prediction process is improved by exploiting the linear structure of Epipolar Plane Images (EPI) in a slope based prediction scheme. The prediction is improved further by averaging predictions made using horizontal and verticals EPIs. Besides this, the difference in these predictions is included in the error energy function, and the texture context is redefined to improve the overall compression ratio.

Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding, such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity.